diff --git a/pixi.lock b/pixi.lock
index f0601b2..b418bda 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -10,11 +10,18 @@ environments:
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-  - arrow-cpp <0.0a0
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   license: BSD-3-Clause
+  license_family: BSD
   purls: []
-  size: 67656
-  timestamp: 1764721105300
+  size: 67438
+  timestamp: 1765819100043
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlicommon-1.2.0-h8616949_1.conda
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-  md5: 4ebb29d020eb3c2c8ac9674d8cfa4a31
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.11.0-5_h9b27e0a_openblas.conda
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+  - libblas 3.11.0 5_he492b99_openblas
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-  - liblapacke 3.11.0   4*_openblas
-  - liblapack  3.11.0   4*_openblas
-  - blas 2.304   openblas
+  - liblapack  3.11.0   5*_openblas
+  - blas 2.305   openblas
+  - liblapacke 3.11.0   5*_openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-3_h2a3cdd5_mkl.conda
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-  md5: b7ac6152e3268dca486e4ef415b82269
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+  build_number: 5
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+  md5: b3fa8e8b55310ba8ef0060103afb02b5
   depends:
-  - libblas 3.11.0 3_hf2e6a31_mkl
+  - libblas 3.11.0 5_hf2e6a31_mkl
   constrains:
-  - blas 2.303   mkl
-  - liblapack  3.11.0   3*_mkl
-  - liblapacke 3.11.0   3*_mkl
+  - liblapack  3.11.0   5*_mkl
+  - liblapacke 3.11.0   5*_mkl
+  - blas 2.305   mkl
   license: BSD-3-Clause
+  license_family: BSD
   purls: []
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-  timestamp: 1764721142545
+  size: 68079
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libcrc32c-1.1.2-he49afe7_0.tar.bz2
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/libcurl-8.17.0-h7dd4100_1.conda
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-  md5: 9ddfaeed0eafce233ae8f4a430816aa5
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libcurl-8.18.0-h9348e2b_0.conda
+  sha256: 1a0af3b7929af3c5893ebf50161978f54ae0256abb9532d4efba2735a0688325
+  md5: de1910529f64ba4a9ac9005e0be78601
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   - krb5 >=1.21.3,<1.22.0a0
@@ -2990,11 +3786,11 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libcurl-8.17.0-h43ecb02_0.conda
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-  md5: cfade9be135edb796837e7d4c288c0fb
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libcurl-8.18.0-h43ecb02_0.conda
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+  md5: 2688214a9bee5d5650cd4f5f6af5c8f2
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   - libssh2 >=1.11.1,<2.0a0
@@ -3005,18 +3801,18 @@ packages:
   license: curl
   license_family: MIT
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-21.1.8-h3d58e20_0.conda
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+  md5: 9f8a60a77ecafb7966ca961c94f33bd1
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   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
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-  timestamp: 1764676139160
+  size: 569777
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.25-h517ebb2_0.conda
   sha256: 025f8b1e85dd8254e0ca65f011919fb1753070eb507f03bca317871a884d24de
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_15.conda
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-  md5: e05ab7ace69b10ae32f8a710a5971f4f
+- conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_16.conda
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+  md5: 1edb8bd8e093ebd31558008e9cb23b47
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   - libwinpthread >=12.0.0.r4.gg4f2fc60ca
   constrains:
-  - libgcc-ng ==15.2.0=*_15
+  - libgomp 15.2.0 h8ee18e1_16
+  - libgcc-ng ==15.2.0=*_16
   - msys2-conda-epoch <0.0a0
-  - libgomp 15.2.0 h8ee18e1_15
   license: GPL-3.0-only WITH GCC-exception-3.1
+  license_family: GPL
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/libgdal-core-3.12.0-hfd904f9_2.conda
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-  md5: 2394c1d85bb5c80355fda7ff366c5fc4
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   - libkml >=1.3.0,<1.4.0a0
   - liblzma >=5.8.1,<6.0a0
-  - libpng >=1.6.51,<1.7.0a0
+  - libpng >=1.6.53,<1.7.0a0
   - libspatialite >=5.1.0,<5.2.0a0
   - libsqlite >=3.51.1,<4.0a0
   - libwebp-base >=1.6.0,<2.0a0
@@ -3238,15 +4035,15 @@ packages:
   - xerces-c >=3.3.0,<3.4.0a0
   - zstd >=1.5.7,<1.6.0a0
   constrains:
-  - libgdal 3.12.0.*
+  - libgdal 3.12.1.*
   license: MIT
   license_family: MIT
   purls: []
-  size: 10709925
-  timestamp: 1764691561079
-- conda: https://conda.anaconda.org/conda-forge/win-64/libgdal-core-3.12.0-hd4e8292_2.conda
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-  md5: 851d7058b2e8040831ad90071683010e
+  size: 10730106
+  timestamp: 1766093828044
+- conda: https://conda.anaconda.org/conda-forge/win-64/libgdal-core-3.12.1-h4c6072a_0.conda
+  sha256: 6e016ae30f9e74038dac1bc6541d38ae806f21a9da9307675591d648bb837ac4
+  md5: cfc8f1a9b92c8ddb31a3e9d0582de2e2
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   - geos >=3.14.1,<3.14.2.0a0
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   - libkml >=1.3.0,<1.4.0a0
   - liblzma >=5.8.1,<6.0a0
-  - libpng >=1.6.51,<1.7.0a0
+  - libpng >=1.6.53,<1.7.0a0
   - libspatialite >=5.1.0,<5.2.0a0
   - libsqlite >=3.51.1,<4.0a0
   - libwebp-base >=1.6.0,<2.0a0
@@ -3278,11 +4075,12 @@ packages:
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   - zstd >=1.5.7,<1.6.0a0
   constrains:
-  - libgdal 3.12.0.*
+  - libgdal 3.12.1.*
   license: MIT
+  license_family: MIT
   purls: []
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-  timestamp: 1764690203971
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+  md5: ab8189163748f95d4cb18ea1952943c3
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   constrains:
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   license: GPL-3.0-only WITH GCC-exception-3.1
+  license_family: GPL
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.12.1-default_h64bd3f2_1002.conda
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-  md5: b0cac6e5b06ca5eeb14b4f7cf908619f
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   - libxml2
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   license_family: BSD
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-  timestamp: 1757624335056
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/libjxl-0.11.1-hde0fb83_8.conda
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-  - libbrotlienc >=1.2.0,<1.3.0a0
   - libcxx >=19
+  - libbrotlienc >=1.2.0,<1.3.0a0
+  - libbrotlidec >=1.2.0,<1.3.0a0
   - libhwy >=1.3.0,<1.4.0a0
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-  - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - libhwy >=1.3.0,<1.4.0a0
+  - libbrotlienc >=1.2.0,<1.3.0a0
+  - libbrotlidec >=1.2.0,<1.3.0a0
   license: BSD-3-Clause
   license_family: BSD
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+  - libblas 3.11.0 5_he492b99_openblas
   constrains:
-  - liblapacke 3.11.0   4*_openblas
-  - libcblas   3.11.0   4*_openblas
-  - blas 2.304   openblas
+  - libcblas   3.11.0   5*_openblas
+  - blas 2.305   openblas
+  - liblapacke 3.11.0   5*_openblas
   license: BSD-3-Clause
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   purls: []
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+- conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-5_hf9ab0e9_mkl.conda
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+  - libblas 3.11.0 5_hf2e6a31_mkl
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-  - liblapacke 3.11.0   3*_mkl
-  - libcblas   3.11.0   3*_mkl
+  - libcblas   3.11.0   5*_mkl
+  - blas 2.305   mkl
+  - liblapacke 3.11.0   5*_mkl
   license: BSD-3-Clause
+  license_family: BSD
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   depends:
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+  - xz 5.8.2.*
   license: 0BSD
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-- conda: https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.1-h2466b09_2.conda
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-  - openmp 21.1.7|21.1.7.*
+  - openmp 21.1.8|21.1.8.*
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+  - python_abi 3.12.* *_cp312
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   - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 3560268
-  timestamp: 1728976534703
+  size: 3598939
+  timestamp: 1766327729418
 - conda: https://conda.anaconda.org/conda-forge/osx-64/xorg-libxau-1.0.12-h8616949_1.conda
   sha256: 928f28bd278c7da674b57d71b2e7f4ac4e7c7ce56b0bf0f60d6a074366a2e76d
   md5: 47f1b8b4a76ebd0cd22bd7153e54a4dc
@@ -6730,6 +8471,31 @@ packages:
   - pkg:pypi/xyzservices?source=hash-mapping
   size: 51128
   timestamp: 1763813786075
+- conda: https://conda.anaconda.org/conda-forge/osx-64/yaml-0.2.5-h4132b18_3.conda
+  sha256: a335161bfa57b64e6794c3c354e7d49449b28b8d8a7c4ed02bf04c3f009953f9
+  md5: a645bb90997d3fc2aea0adf6517059bd
+  depends:
+  - __osx >=10.13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 79419
+  timestamp: 1753484072608
+- conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h6a83c73_3.conda
+  sha256: 80ee68c1e7683a35295232ea79bcc87279d31ffeda04a1665efdb43cbd50a309
+  md5: 433699cba6602098ae8957a323da2664
+  depends:
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 63944
+  timestamp: 1753484092156
 - pypi: https://files.pythonhosted.org/packages/51/38/347d1fcde4edabd338d5872ca5759ccfb95ff1cf5207dafded981fd08c4f/yara_python-4.5.4.tar.gz
   name: yara-python
   version: 4.5.4
@@ -6738,38 +8504,70 @@ packages:
   name: yara-python
   version: 4.5.4
   sha256: bf14a8af06b2b980a889bdc3f9e8ccd6e703d2b3fa1c98da5fd3a1c3b551eb47
-- conda: https://conda.anaconda.org/conda-forge/noarch/yarl-1.22.0-pyh7db6752_0.conda
-  sha256: b04271f56c68483b411c5465afff73b8eabdea564e942f0e7afed06619272635
-  md5: ca3c00c764cee005798a518cba79885c
+- conda: https://conda.anaconda.org/conda-forge/osx-64/yarl-1.22.0-py312hacf3034_0.conda
+  sha256: c030ea7a6f88a54ded713db44420091e1606a04ea57b2cb2b4e00c5c41594929
+  md5: e441d2fc9a075115c08ec037d78d94d9
   depends:
+  - __osx >=10.13
   - idna >=2.0
   - multidict >=4.0
   - propcache >=0.2.1
-  - python >=3.10
-  track_features:
-  - yarl_no_compile
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
   license: Apache-2.0
   license_family: Apache
   purls:
   - pkg:pypi/yarl?source=hash-mapping
-  size: 73066
-  timestamp: 1761337117132
-- conda: https://conda.anaconda.org/conda-forge/osx-64/yarl-1.22.0-py312hacf3034_0.conda
-  sha256: c030ea7a6f88a54ded713db44420091e1606a04ea57b2cb2b4e00c5c41594929
-  md5: e441d2fc9a075115c08ec037d78d94d9
+  size: 143615
+  timestamp: 1761337116037
+- conda: https://conda.anaconda.org/conda-forge/win-64/yarl-1.22.0-py312h05f76fc_0.conda
+  sha256: b622ef03b033a1c3984cb3e47e198370f23bf239c579a0c04f9179237fbb541b
+  md5: d4975947624e265fa594b86ce148a0c1
   depends:
-  - __osx >=10.13
   - idna >=2.0
   - multidict >=4.0
   - propcache >=0.2.1
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
   license: Apache-2.0
   license_family: Apache
   purls:
   - pkg:pypi/yarl?source=hash-mapping
-  size: 143615
-  timestamp: 1761337116037
+  size: 141998
+  timestamp: 1761337573480
+- conda: https://conda.anaconda.org/conda-forge/osx-64/zeromq-4.3.5-h6c33b1e_9.conda
+  sha256: 30aa5a2e9c7b8dbf6659a2ccd8b74a9994cdf6f87591fcc592970daa6e7d3f3c
+  md5: d940d809c42fbf85b05814c3290660f5
+  depends:
+  - __osx >=10.13
+  - libcxx >=19
+  - libsodium >=1.0.20,<1.0.21.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  license: MPL-2.0
+  license_family: MOZILLA
+  purls: []
+  size: 259628
+  timestamp: 1757371000392
+- conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-h5bddc39_9.conda
+  sha256: 690cf749692c8ea556646d1a47b5824ad41b2f6dfd949e4cdb6c44a352fcb1aa
+  md5: a6c8f8ee856f7c3c1576e14b86cd8038
+  depends:
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - libsodium >=1.0.20,<1.0.21.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  license: MPL-2.0
+  license_family: MOZILLA
+  purls: []
+  size: 265212
+  timestamp: 1757370864284
 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
   sha256: b4533f7d9efc976511a73ef7d4a2473406d7f4c750884be8e8620b0ce70f4dae
   md5: 30cd29cb87d819caead4d55184c1d115
@@ -6806,48 +8604,29 @@ packages:
   purls: []
   size: 107439
   timestamp: 1727963788936
-- conda: https://conda.anaconda.org/conda-forge/osx-64/zlib-ng-2.3.2-h53ec75d_0.conda
-  sha256: 9183b2ada178d83ca6f8a66ba2ddcfb5f2476c2e866a4609c1f84dd5f32d796e
-  md5: 1e979f90e823b82604ab1da7e76c75e5
+- conda: https://conda.anaconda.org/conda-forge/osx-64/zlib-ng-2.3.2-h8bce59a_1.conda
+  sha256: 945725769bc668435af1c23733c3c1dba01eb115ad3bad5393c9df2e23de6cfc
+  md5: cdd69480d52f2b871fad1a91324d9942
   depends:
   - __osx >=10.13
   - libcxx >=19
   license: Zlib
+  license_family: Other
   purls: []
-  size: 135199
-  timestamp: 1764716055794
-- conda: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.2-h5112557_0.conda
-  sha256: 331e63a801efc9aa47e0a7f7be5becc81d9c52c1163308182078108e003c12e5
-  md5: 2b4f8712b09b5fd3182cda872ce8482c
+  size: 120585
+  timestamp: 1766077108928
+- conda: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.2-h0261ad2_1.conda
+  sha256: e058e925bed8d9e5227cecc098e02992813046fd89206194435e975a9f6eff56
+  md5: bc2fba648e1e784c549e20bbe1a8af40
   depends:
+  - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
-  - ucrt >=10.0.20348.0
   license: Zlib
+  license_family: Other
   purls: []
-  size: 134848
-  timestamp: 1764715928393
-- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.25.0-py312he5662c2_1.conda
-  sha256: 49241574c373331ae63d9cb4978836db3b2571176a7db81fe48436c84ce38ff4
-  md5: e9e25949b682e95535068bae33153ba6
-  depends:
-  - python
-  - cffi >=1.11
-  - zstd >=1.5.7,<1.5.8.0a0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  - ucrt >=10.0.20348.0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  - ucrt >=10.0.20348.0
-  - zstd >=1.5.7,<1.6.0a0
-  - python_abi 3.12.* *_cp312
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 374949
-  timestamp: 1762512770373
+  size: 123890
+  timestamp: 1766076739436
 - conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h3eecb57_6.conda
   sha256: 47101a4055a70a4876ffc87b750ab2287b67eca793f21c8224be5e1ee6394d3f
   md5: 727109b184d680772e3122f40136d5ca
@@ -6868,6 +8647,7 @@ packages:
   - ucrt >=10.0.20348.0
   - libzlib >=1.3.1,<2.0a0
   license: BSD-3-Clause
+  license_family: BSD
   purls: []
   size: 388453
   timestamp: 1764777142545
diff --git a/pixi.toml b/pixi.toml
index bbdf2aa..5cca1e0 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -17,21 +17,29 @@ pandas = ">=2.3.3,<3"
 requests = ">=2.32.5,<3"
 plotly = ">=6.5.0,<7"
 geoip2 = ">=4.8.0,<5"
-gdal = ">=3.12.0,<4"
+gdal = ">=3.12.1,<4"
 pandas-stubs = ">=2.3.3.251201,<3"
-pyarrow = ">=22.0.0,<23"
+pyarrow = ">=23.0.0,<24"
 geopandas = ">=1.1.1,<2"
 geocoder = ">=1.38.1,<2"
 pyrosm = ">=0.6.2,<0.7"
 curl = ">=8.17.0,<9"
-textstat = ">=0.7.11,<0.8"
+textstat = ">=0.7.12,<0.8"
 nltk = ">=3.9.2,<4"
 spacy = ">=3.8.11,<4"
 lingua-language-detector = ">=1.3.4,<2"
 google-cloud-translate = ">=3.23.0,<4"
+sqlite = ">=3.51.2,<4"
+sklearn-compat = ">=0.1.5,<0.2"
+sklearn-pandas = ">=2.2.0,<3"
+scikit-learn = ">=1.8.0,<2"
+pixi-kernel = ">=0.7.1,<0.8"
+libsqlite = ">=3.51.2,<4"
+sqlite-utils = ">=3.39,<4"
 
 [pypi-dependencies]
 scipy = "*"
+statsmodels = "*"
 chardet = ">=5.2.0, <6"
 charset-normalizer = "*"
 ftfy = ">=6.3.1, <7"
@@ -39,3 +47,5 @@ yara-python = ">=4.5.4, <5"
 ioc-finder = ">=5.0.3, <6"
 validators = ">=0.35.0, <0.36"
 deep-translator = ">=1.11.4, <2"
+prince = ">=0.16.5, <0.17"
+user-agents = ">=2.2.0, <3"
diff --git a/script.py b/script.py
index 03c414e..09d46aa 100644
--- a/script.py
+++ b/script.py
@@ -1,13 +1,39 @@
-#%% library init
+# %% [markdown]
+# # Cybersecurity Attacks Dataset - Exploratory Data Analysis
+#
+# This notebook performs comprehensive EDA on a cybersecurity attacks dataset,
+# including IP geolocation analysis, attack pattern visualization, and
+# statistical analysis of network traffic features.
+
+# %% Library Initialization
+# =============================================================================
+# LIBRARY IMPORTS AND CONFIGURATION
+# =============================================================================
+# This cell imports all required libraries and configures the environment:
+# - pandas: Data manipulation and analysis
+# - prince: Multiple Correspondence Analysis (MCA) for categorical data
+# - plotly: Interactive visualizations
+# - geoip2/django: IP geolocation services
+# - sklearn: Machine learning metrics (Matthews correlation)
+# - user_agents: Browser/device detection from User-Agent strings
 
 import pandas as pd
 import prince
 import plotly.io
 import os
 import numpy as np
-os.environ[ "GDAL_LIBRARY_PATH" ] = "C:/Users/KalooIna/anaconda3/envs/cybersecurity_attacks/Library/bin/gdal311.dll" # make sure this is the name of your gdal.dll file ( rename it to appropriate version if necessary )
+
+# Optional GDAL configuration for geospatial operations
+# Uncomment and adjust path if needed for your environment
+"""
+os.environ["GDAL_LIBRARY_PATH"] = (
+    "C:/Users/KalooIna/anaconda3/envs/cybersecurity_attacks/Library/bin/gdal311.dll"
+) """
 import geoip2.database
-plotly.io.renderers.default = "browser" # plotly settings for browser settings
+
+# Configure Plotly to render charts in the default browser
+plotly.io.renderers.default = "browser"
+
 import plotly.express as px
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots as subp
@@ -17,1177 +43,2098 @@
 from user_agents import parse
 from user_agents import parse as ua_parse
 from django.conf import settings
-# django settings for geoIP2
-settings.configure(
-    GEOIP_PATH = "data/geolite2_db" ,
-    INSTALLED_APPS = [ "django.contrib.gis" ]
+from django.contrib.gis.geoip2 import GeoIP2
+
+# -----------------------------------------------------------------------------
+# Django GeoIP2 Configuration
+# Configure Django settings for IP geolocation using MaxMind GeoLite2 database
+# The database must be downloaded from MaxMind and placed in ./geolite2_db/
+# -----------------------------------------------------------------------------
+if not settings.configured:
+    settings.configure(
+        GEOIP_PATH="./geolite2_db", INSTALLED_APPS=["django.contrib.gis"]
     )
 django.setup()
-from django.contrib.gis.geoip2 import GeoIP2
+
+# Initialize GeoIP2 lookup service
 geoIP = GeoIP2()
 
-# useful links
+# Reference URLs for GeoIP2 setup and documentation
 maxmind_geoip2_db_url = "https://www.maxmind.com/en/accounts/1263991/geoip/downloads"
 geoip2_doc_url = "https://geoip2.readthedocs.io/en/latest/"
 geoip2_django_doc_url = "https://docs.djangoproject.com/en/5.2/ref/contrib/gis/geoip2/"
 
-# loadding dataset
-# df = pd.read_csv( "data/cybersecurity_attacks.csv" )
-df = pd.read_csv( "data/df.csv" , sep = "|" , index_col = 0 )
-
-# transform categorical variable to binary variables [ 0 , 1 ]
-def catvar_mapping( col_name , values , name = None) : 
-    df1 = df.copy( deep = True )
-    if name is None :
+# -----------------------------------------------------------------------------
+# Load Dataset
+# The dataset contains network traffic records with attack classifications
+# Columns include: timestamps, IP addresses, ports, protocols, attack types, etc.
+# -----------------------------------------------------------------------------
+df = pd.read_csv("data/df.csv", sep="|", index_col=0)
+
+
+# =============================================================================
+# UTILITY FUNCTIONS
+# =============================================================================
+
+def catvar_mapping(col_name, values, name=None):
+    """
+    Transform categorical variables to binary (0/1) indicator columns.
+
+    This function performs one-hot style encoding for specified categorical values,
+    creating new binary columns that indicate presence/absence of each category.
+
+    Parameters:
+    -----------
+    col_name : str
+        Name of the categorical column to transform
+    values : list
+        List of categorical values to create binary indicators for
+    name : list, optional
+        Custom names for the new binary columns. If None, uses the values.
+        Use ["/"] to replace the original column in-place.
+
+    Returns:
+    --------
+    DataFrame
+        Copy of the dataframe with new binary columns added
+
+    Example:
+    --------
+    # Create "Protocol UDP" and "Protocol ICMP" binary columns
+    df = catvar_mapping("Protocol", ["UDP", "ICMP"])
+    """
+    df1 = df.copy(deep=True)
+    if name is None:
         name = values
-    elif ( len( name ) == 1 ) and ( name != [ "/" ]) :
-        col_target = f"{ col_name } { name[ 0 ]}"
-        df1 = df1.rename( columns = { col_name : col_target })
+    elif (len(name) == 1) and (name != ["/"]):
+        col_target = f"{col_name} {name[0]}"
+        df1 = df1.rename(columns={col_name: col_target})
         col_name = col_target
-        name = [ col_target ]
-    col = df1.columns.get_loc( col_name ) + 1
-    for val , nm in zip( values , name ) :
-        if ( nm == "/" ) :
+        name = [col_target]
+    col = df1.columns.get_loc(col_name) + 1
+    for val, nm in zip(values, name):
+        if nm == "/":
             col_target = col_name
-        elif ( len( name ) == 1 ) :
+        elif len(name) == 1:
             col_target = nm
-        else :
-            col_target = f"{ col_name } { nm }"
-            df1.insert( col , col_target , value = pd.NA )
-        bully = df1[ col_name ] == val
-        df1.loc[ bully , col_target ] = 1
-        df1.loc[ ~ bully , col_target ] = 0
+        else:
+            col_target = f"{col_name} {nm}"
+            if col_target not in df1.columns:
+                df1.insert(col, col_target, value=pd.NA)
+        bully = df1[col_name] == val
+        df1.loc[bully, col_target] = 1
+        df1.loc[~bully, col_target] = 0
         col += 1
     return df1
-        
-# pieichart generator for a column
-def piechart_col( col , names = None ) :
-    if names is None :
-        fig = px.pie( 
-            values = df[ col ].value_counts() ,
-            names = df[ col ].value_counts().index ,
-            )
+
+
+def piechart_col(col, names=None):
+    """
+    Generate an interactive pie chart for a categorical column.
+
+    Parameters:
+    -----------
+    col : str
+        Column name to visualize
+    names : list, optional
+        Custom labels for pie chart segments. If None, uses actual values.
+    """
+    if names is None:
+        fig = px.pie(
+            values=df[col].value_counts(),
+            names=df[col].value_counts().index,
+            title=f"Distribution of {col}",
+        )
+        fig.update_traces(
+            textposition='inside',
+            textinfo='percent+label',
+            hovertemplate="<b>%{label}</b><br>Count: %{value}<br>Percentage: %{percent}<extra></extra>"
+        )
         fig.show()
-    else :
-        fig = px.pie( 
-            values = df[ col ].value_counts() ,
-            names = names ,
-            )
+    else:
+        fig = px.pie(
+            values=df[col].value_counts(),
+            names=names,
+            title=f"Distribution of {col}",
+        )
+        fig.update_traces(
+            textposition='inside',
+            textinfo='percent+label',
+            hovertemplate="<b>%{label}</b><br>Count: %{value}<br>Percentage: %{percent}<extra></extra>"
+        )
         fig.show()
 
-# transforms IP addresses to infos : longitude , latitude , country , city
-def ip_to_coords( ip_address ) :
-    print( ip_address )
-    ret = pd.Series( dtype = object )
-    try :
-        res = geoIP.geos( ip_address ).wkt
-        lon , lat = res.replace( "(" , "" ).replace( ")" , "" ).split()[ 1 : ]
-        ret = pd.concat([ ret , pd.Series([ lat , lon ])] , ignore_index = True )
-    except :
-        ret = pd.concat([ ret , pd.Series([ pd.NA , pd.NA ])] , ignore_index = True )
-    try :
-        res = geoIP.city( ip_address )
-        ret = pd.concat([ ret , pd.Series([ res[ "country_name" ] , res[ "city" ]])] , ignore_index = True )
-    except :
-        ret = pd.concat([ ret , pd.Series([ pd.NA , pd.NA ])] , ignore_index = True )
+
+def ip_to_coords(ip_address):
+    """
+    Convert an IP address to geographic coordinates and location info.
+
+    Uses MaxMind GeoIP2 database to lookup geographic information for IP addresses.
+
+    Parameters:
+    -----------
+    ip_address : str
+        IPv4 or IPv6 address to geolocate
+
+    Returns:
+    --------
+    pd.Series
+        Series containing [latitude, longitude, country_name, city]
+        Returns NA values for any fields that cannot be resolved
+    """
+    print(ip_address)
+    ret = pd.Series(dtype=object)
+    try:
+        res = geoIP.geos(ip_address).wkt
+        lon, lat = res.replace("(", "").replace(")", "").split()[1:]
+        ret = pd.concat([ret, pd.Series([lat, lon])], ignore_index=True)
+    except:
+        ret = pd.concat([ret, pd.Series([pd.NA, pd.NA])], ignore_index=True)
+    try:
+        res = geoIP.city(ip_address)
+        ret = pd.concat(
+            [ret, pd.Series([res["country_name"], res["city"]])], ignore_index=True
+        )
+    except:
+        ret = pd.concat([ret, pd.Series([pd.NA, pd.NA])], ignore_index=True)
     return ret
-       
-        
-#%% EDA
-     
-# renaming columns
-df = df.rename( columns = { 
-    "Timestamp" : "date" ,
-    "Source Port" : "Source Port ephemeral" ,
-    "Destination Port" : "Destination Port ephemeral" ,
-    "Alerts/Warnings" : "Alert Trigger" ,
-    })
-df = df.drop( "User Information" , axis = 1 )
-print( df.describe())
-
-# generation of crosstables for cat variables
+
+
+# %% [markdown]
+# ## Exploratory Data Analysis (EDA)
+# This section performs initial data exploration:
+# - Column renaming for clarity
+# - Missing value analysis
+# - Target variable distribution (Attack Type)
+# - Temporal distribution of attacks
+
+# %% EDA - Data Preparation and Overview
+# =============================================================================
+# DATA PREPARATION
+# =============================================================================
+# Rename columns for better readability and consistency
+# - "Timestamp" -> "date" for temporal analysis
+# - Port columns renamed to indicate ephemeral port analysis
+# - Alerts/Warnings -> Alert Trigger for binary encoding
+df = df.rename(
+    columns={
+        "Timestamp": "date",
+        "Source Port": "Source Port ephemeral",
+        "Destination Port": "Destination Port ephemeral",
+        "Alerts/Warnings": "Alert Trigger",
+    }
+)
+
+# Display summary statistics for numerical columns
+print("=" * 60)
+print("DATASET SUMMARY STATISTICS")
+print("=" * 60)
+print(df.describe())
+
+# -----------------------------------------------------------------------------
+# Cross-tabulation utility function
+# Used to analyze relationships between categorical variables
+# -----------------------------------------------------------------------------
 crosstabs = {}
-def crosstab_col( col ,target , name_col , name_target ) :
-    name_tab = f"{ name_col }_x_{ name_target }"
-    crosstabs[ name_tab ] = pd.crosstab( df[ target ] , df[ col ] , normalize = True ) * 100
-
-# NAs
-df_s0 = df.shape[ 0 ]
-for col in df.columns :
-    NA_n = sum( df[ col ].isna())
-    if NA_n > 0 :
-        print( f"number of NAs in { col } = { NA_n } / { df_s0 } = { NA_n / df_s0 } " )
-
-# Attack Type !!!! TARGET VARIABLE !!!!
-col_name = "Attack Type"
-print( df[ col_name ].value_counts())
-piechart_col( col_name )
 
-# date
+
+def crosstab_col(col, target, name_col, name_target):
+    """
+    Create a normalized cross-tabulation between two categorical columns.
+
+    Parameters:
+    -----------
+    col : str
+        Column name for the independent variable
+    target : str
+        Column name for the dependent/target variable
+    name_col, name_target : str
+        Short names for labeling the crosstab result
+    """
+    name_tab = f"{name_col}_x_{name_target}"
+    crosstabs[name_tab] = pd.crosstab(df[target], df[col], normalize=True) * 100
+
+
+# -----------------------------------------------------------------------------
+# Missing Value Analysis
+# Identify columns with missing data and their percentages
+# -----------------------------------------------------------------------------
+print("\n" + "=" * 60)
+print("MISSING VALUE ANALYSIS")
+print("=" * 60)
+df_s0 = df.shape[0]
+for col in df.columns:
+    NA_n = sum(df[col].isna())
+    if NA_n > 0:
+        print(f"Missing values in {col}: {NA_n:,} / {df_s0:,} ({100*NA_n/df_s0:.2f}%)")
+
+# -----------------------------------------------------------------------------
+# TARGET VARIABLE ANALYSIS: Attack Type
+# This is the primary classification target with 3 classes:
+# - Malware: Malicious software attacks
+# - Intrusion: Unauthorized access attempts
+# - DDoS: Distributed Denial of Service attacks
+# -----------------------------------------------------------------------------
+print("\n" + "=" * 60)
+print("TARGET VARIABLE: Attack Type Distribution")
+print("=" * 60)
+col_name = "Attack Type"
+print(df[col_name].value_counts())
+piechart_col(col_name)
+
+# -----------------------------------------------------------------------------
+# TEMPORAL ANALYSIS: Attack Distribution Over Time
+# Analyze when attacks occurred to identify patterns or trends
+# -----------------------------------------------------------------------------
+print("\n" + "=" * 60)
+print("TEMPORAL DISTRIBUTION")
+print("=" * 60)
 col_name = "date"
-df[ col_name ] = pd.to_datetime( df[ col_name ])
-date_end = max( df[ col_name ])
-date_start = min( df[ col_name ])
-print( f"dates go from { date_start } and { date_end }" )
-fig = px.histogram( df , col_name )
+df[col_name] = pd.to_datetime(df[col_name])
+date_end = max(df[col_name])
+date_start = min(df[col_name])
+print(f"Dataset time range: {date_start} to {date_end}")
+print(f"Duration: {(date_end - date_start).days} days")
+
+fig = px.histogram(
+    df,
+    x=col_name,
+    title="Distribution of Cyber Attacks Over Time",
+    labels={"date": "Date", "count": "Number of Attacks"},
+    color_discrete_sequence=["#636EFA"]
+)
+fig.update_layout(
+    xaxis_title="Date",
+    yaxis_title="Number of Attacks",
+    showlegend=False
+)
 fig.show()
 
 
-#%% IP address
+# %% [markdown]
+# ## IP Address Geolocation Analysis
+# This section:
+# - Extracts geographic information from Source and Destination IP addresses
+# - Visualizes global distribution of attack origins and targets
+# - Analyzes proxy usage patterns in the network traffic
+
+# %% IP Address Geolocation
+# =============================================================================
+# IP ADDRESS GEOLOCATION EXTRACTION
+# =============================================================================
+# Convert IP addresses to geographic coordinates using MaxMind GeoIP2
+# This enables geographic visualization and analysis of attack patterns
+
+print("=" * 60)
+print("IP ADDRESS GEOLOCATION PROCESSING")
+print("=" * 60)
 
 i = 2
-for destsource in [ "Source" , "Destination" ] :
-    col = df.columns.get_loc( f"{ destsource } IP Address" ) + 1
-    df.insert( col , f"{ destsource } IP latitude" , value = pd.NA )
-    df.insert( col + 1 , f"{ destsource } IP longitude" , value = pd.NA )
-    df.insert( col + 2 , f"{ destsource } IP country" , value = pd.NA )
-    df.insert( col + 3 , f"{ destsource } IP city" , value = pd.NA )
-    df[[ f"{ destsource } IP latitude" , f"{ destsource } IP longitude" , f"{ destsource } IP country" , f"{ destsource } IP city" ]] = df[ f"{ destsource } IP Address" ].apply( lambda x : ip_to_coords( x ))
-
-## IP address map graph
+for destsource in ["Source", "Destination"]:
+    geo_columns = [
+        f"{destsource} IP latitude",
+        f"{destsource} IP longitude",
+        f"{destsource} IP country",
+        f"{destsource} IP city",
+    ]
+
+    # Skip geolocation if already processed (columns exist)
+    if all(col in df.columns for col in geo_columns):
+        print(f"{destsource} IP geolocation already processed - skipping")
+        continue
+
+    print(f"Processing {destsource} IP addresses...")
+    col = df.columns.get_loc(f"{destsource} IP Address") + 1
+
+    # Insert new columns for geographic data
+    for i, geo_col in enumerate(geo_columns):
+        if geo_col not in df.columns:
+            df.insert(col + i, geo_col, value=pd.NA)
+
+    # Apply geolocation lookup to each IP address
+    geo_data = df[f"{destsource} IP Address"].apply(ip_to_coords)
+    geo_df = pd.DataFrame(geo_data.tolist(), index=df.index)
+
+    df[geo_columns[0]] = geo_df[0]  # Latitude
+    df[geo_columns[1]] = geo_df[1]  # Longitude
+    df[geo_columns[2]] = geo_df[2]  # Country
+    df[geo_columns[3]] = geo_df[3]  # City
+
+# -----------------------------------------------------------------------------
+# GLOBAL IP DISTRIBUTION MAP
+# Orthographic projection showing attack source and destination locations
+# Left globe: Source IPs (where attacks originate)
+# Right globe: Destination IPs (attack targets)
+# -----------------------------------------------------------------------------
 fig = subp(
-    rows = 1 ,
-    cols = 2 ,
-    specs = [[
-        { "type" : "scattergeo" } , 
-        { "type" : "scattergeo" } ,
-        ]] ,
-    subplot_titles = (
-        "Source IP locations" ,
-        "Destination IP locations" 
-        )
-    )
+    rows=1,
+    cols=2,
+    specs=[
+        [
+            {"type": "scattergeo"},
+            {"type": "scattergeo"},
+        ]
+    ],
+    subplot_titles=(
+        "Attack Origin Locations (Source IPs)",
+        "Attack Target Locations (Destination IPs)"
+    ),
+)
+
+# Source IP locations - Blue markers
 fig.add_trace(
     go.Scattergeo(
-        lat = df[ "Source IP latitude" ] ,
-        lon = df[ "Source IP longitude" ] ,
-        mode = "markers" ,
-        marker = { 
-            "size" : 5 ,
-            "color" : "blue"
-            }
-        ) ,
-    row = 1 , 
-    col = 1
-    )
+        lat=df["Source IP latitude"],
+        lon=df["Source IP longitude"],
+        mode="markers",
+        marker={"size": 5, "color": "blue", "opacity": 0.6},
+        name="Source IPs",
+        hovertemplate="<b>Attack Origin</b><br>Lat: %{lat:.2f}<br>Lon: %{lon:.2f}<extra></extra>"
+    ),
+    row=1,
+    col=1,
+)
+
+# Destination IP locations - Blue markers
 fig.add_trace(
     go.Scattergeo(
-        lat = df[ "Destination IP latitude" ] ,
-        lon = df[ "Destination IP longitude" ] ,
-        mode = "markers" ,
-        marker = { 
-            "size" : 5 ,
-            "color" : "blue"
-            }
-        ) ,
-    row = 1 , 
-    col = 2
-    )
+        lat=df["Destination IP latitude"],
+        lon=df["Destination IP longitude"],
+        mode="markers",
+        marker={"size": 5, "color": "blue", "opacity": 0.6},
+        name="Destination IPs",
+        hovertemplate="<b>Attack Target</b><br>Lat: %{lat:.2f}<br>Lon: %{lon:.2f}<extra></extra>"
+    ),
+    row=1,
+    col=2,
+)
+
 fig.update_geos(
-    projection_type = "orthographic" ,
-    showcountries = True ,
-    showland = True ,
-    # landcolor = "LightGreen"
+    projection_type="orthographic",
+    showcountries=True,
+    showland=True,
+    landcolor="lightgray",
+    countrycolor="white",
+    oceancolor="lightblue",
+    showocean=True
 )
 fig.update_layout(
-    height = 750 ,
-    margin = { 
-        "r" : 0 , 
-        "t" : 80 , 
-        "l" : 0 , 
-        "b" : 0 
-        } ,
-    title_text = "IP Address Locations" ,
-    title_x = 0.5
+    height=750,
+    margin={"r": 0, "t": 80, "l": 0, "b": 0},
+    title_text="Global Distribution of Cyber Attack Traffic",
+    title_x=0.5,
+    title_font_size=18,
+    showlegend=True,
+    legend=dict(
+        orientation="h",
+        yanchor="bottom",
+        y=-0.1,
+        xanchor="center",
+        x=0.5
+    )
 )
 fig.show()
 
-# Proxy Information
+# -----------------------------------------------------------------------------
+# PROXY INFORMATION COLUMNS
+# Prepare columns for proxy IP geolocation data
+# Proxies may be used to mask the true origin of attacks
+# -----------------------------------------------------------------------------
 col_name = "Proxy Information"
-# print( df[ col_name ].value_counts())
-col = df.columns.get_loc( col_name )
-df.insert( col + 1 , "Proxy latitude" , value = pd.NA )
-df.insert( col + 2 , "Proxy longitude" , value = pd.NA )
-df.insert( col + 3 , "Proxy country" , value = pd.NA )
-df.insert( col + 4 , "Proxy city" , value = pd.NA )
-df[[ "Proxy latitude" , "Proxy longitude" , "Proxy country" , "Proxy city" ]] = df[ "Proxy Information" ].apply( lambda x : ip_to_coords( x ))
-
-def sankey_diag_IPs( ntop ) :
+col = df.columns.get_loc(col_name)
+
+for i, new_col in enumerate(
+    ["Proxy latitude", "Proxy longitude", "Proxy country", "Proxy city"], start=1
+):
+    if new_col not in df.columns:
+        df.insert(col + i, new_col, value=pd.NA)
+
+
+def sankey_diag_IPs(ntop):
+    """
+    Create a Sankey diagram showing traffic flow between Source, Proxy, and Destination countries.
+
+    This visualization shows how network traffic flows geographically:
+    - Left nodes: Source IP countries (where traffic originates)
+    - Middle nodes: Proxy countries (intermediate routing points)
+    - Right nodes: Destination IP countries (final targets)
+
+    Parameters:
+    -----------
+    ntop : int
+        Number of top countries to display (others grouped as "other")
+
+    Returns:
+    --------
+    DataFrame
+        Aggregated IP flow counts grouped by source, proxy, and destination
+    """
     IPs_col = {}
-    labels = pd.Series( dtype = "string" )
-    for IPid , dfcol in zip([ "SIP" , "DIP" , "PIP" ] , [ "Source IP country" , "Destination IP country" , "Proxy country" ]) :
-        IPs_col[ IPid ] = df[ dfcol ].copy( deep = True )
-        IPs_col[ f"{ IPid }labs" ] = pd.Series( IPs_col[ IPid ].value_counts().index[ : ntop ])
-        bully = ( IPs_col[ IPid ].isin( IPs_col[ f"{IPid}labs" ]) | IPs_col[ IPid ].isna())
-        IPs_col[ IPid ].loc[ ~ bully ] = "other"
-        IPs_col[ f"{ IPid }labs" ] = f"{ IPid } " + pd.concat([ IPs_col[ f"{ IPid }labs" ] , pd.Series([ "other" ])])
-        labels = pd.concat([ labels , IPs_col[ f"{ IPid }labs" ]])
-    labels = list( labels.reset_index( drop = True ))
-    
-    aggregIPs = pd.DataFrame({
-        "SIP" : IPs_col[ "SIP" ] ,
-        "PIP" : IPs_col[ "PIP" ] ,
-        "DIP" : IPs_col[ "DIP" ] ,
-        })
-    aggregIPs = aggregIPs.groupby( by = [ 
-        "SIP" , 
-        "PIP" , 
-        "DIP"
-        ]).size().to_frame( "count" )
+    labels = pd.Series(dtype="string")
+
+    # Process each IP type: Source (SIP), Destination (DIP), Proxy (PIP)
+    for IPid, dfcol in zip(
+        ["SIP", "DIP", "PIP"],
+        ["Source IP country", "Destination IP country", "Proxy country"],
+    ):
+        IPs_col[IPid] = df[dfcol].copy(deep=True)
+        IPs_col[f"{IPid}labs"] = pd.Series(IPs_col[IPid].value_counts().index[:ntop])
+        bully = IPs_col[IPid].isin(IPs_col[f"{IPid}labs"]) | IPs_col[IPid].isna()
+        IPs_col[IPid].loc[~bully] = "other"
+        IPs_col[f"{IPid}labs"] = f"{IPid} " + pd.concat(
+            [IPs_col[f"{IPid}labs"], pd.Series(["other"])]
+        )
+        labels = pd.concat([labels, IPs_col[f"{IPid}labs"]])
+    labels = list(labels.reset_index(drop=True))
+
+    # Aggregate traffic flows between countries
+    aggregIPs = pd.DataFrame(
+        {
+            "SIP": IPs_col["SIP"],
+            "PIP": IPs_col["PIP"],
+            "DIP": IPs_col["DIP"],
+        }
+    )
+    aggregIPs = aggregIPs.groupby(by=["SIP", "PIP", "DIP"]).size().to_frame("count")
 
-    # computation of source , target , value
+    # Build Sankey diagram links
     source = []
     target = []
     value = []
     nlvl = aggregIPs.index.nlevels
-    for idx , row in aggregIPs.iterrows()  :
+    for idx, row in aggregIPs.iterrows():
         row_labs = []
-        if ( nlvl == 1 ) :
-            row_labs.append( f"{ aggregIPs.index.name } { idx }" )
-        else :
-            for i , val in enumerate( idx ) :
-                row_labs.append( f"{ aggregIPs.index.names[ i ] } { val }" )
-        for i in range( 0 , nlvl - 1 ) :
-            source.append( labels.index( row_labs[ i ]))
-            target.append( labels.index( row_labs[ i + 1 ]))
-            value.append( row.item() )
-    
-    # plot the sankey diagram
-    n = len( labels )
+        if nlvl == 1:
+            row_labs.append(f"{aggregIPs.index.name} {idx}")
+        else:
+            for i, val in enumerate(idx):
+                row_labs.append(f"{aggregIPs.index.names[i]} {val}")
+        for i in range(0, nlvl - 1):
+            source.append(labels.index(row_labs[i]))
+            target.append(labels.index(row_labs[i + 1]))
+            value.append(row.item())
+
+    # Create Sankey diagram with Inferno color scale
+    n = len(labels)
     colors = px.colors.sample_colorscale(
-        px.colors.sequential.Inferno ,
-        [ i / ( n - 1 ) for i in range( n )]
-        ) 
-    fig = go.Figure( data = [ go.Sankey(
-        node = dict(
-          pad = 15 ,
-          thickness = 20 ,
-          line = dict( 
-              color = "rgba( 0 , 0 , 0 , 0.1 )" ,
-              width = 0.5 
-              ) ,
-          label = labels ,
-          color = colors ,
-          ) ,
-        link = dict(
-          source = source ,
-          target = target ,
-          value = value 
-          ))])
+        px.colors.sequential.Inferno, [i / (n - 1) for i in range(n)]
+    )
+    fig = go.Figure(
+        data=[
+            go.Sankey(
+                node=dict(
+                    pad=15,
+                    thickness=20,
+                    line=dict(color="rgba(0, 0, 0, 0.1)", width=0.5),
+                    label=labels,
+                    color=colors,
+                    hovertemplate="<b>%{label}</b><br>Total flows: %{value}<extra></extra>"
+                ),
+                link=dict(
+                    source=source,
+                    target=target,
+                    value=value,
+                    hovertemplate="<b>Flow</b><br>From: %{source.label}<br>To: %{target.label}<br>Count: %{value}<extra></extra>"
+                ),
+            )
+        ]
+    )
     fig.update_layout(
-        title_text = "Sankey Diagram" ,
-        # font_family = "Courier New" ,
-        # font_color = "blue" , 
-        font_size = 20 ,
-        title_font_family = "Avenir" ,
-        title_font_color = "black",
-        )
+        title_text="Network Traffic Flow: Source → Proxy → Destination Countries",
+        title_font_size=18,
+        font_size=14,
+        title_font_family="Avenir",
+        title_font_color="black",
+        annotations=[
+            dict(x=0.01, y=1.05, text="<b>Source Countries</b>", showarrow=False, font_size=12),
+            dict(x=0.5, y=1.05, text="<b>Proxy Countries</b>", showarrow=False, font_size=12),
+            dict(x=0.99, y=1.05, text="<b>Destination Countries</b>", showarrow=False, font_size=12),
+        ]
+    )
     fig.show()
-    
+
     return aggregIPs
-aggregIPs = sankey_diag_IPs( 10 )
 
-#%% Source Port
+
+# Generate IP traffic flow Sankey diagram with top 10 countries
+print("\n" + "=" * 60)
+print("IP TRAFFIC FLOW ANALYSIS")
+print("=" * 60)
+aggregIPs = sankey_diag_IPs(10)
+
+# %% [markdown]
+# ## Network Protocol and Port Analysis
+# This section analyzes network layer characteristics:
+# - Port classification (ephemeral vs registered)
+# - Protocol distribution (TCP, UDP, ICMP)
+# - Packet types and traffic categories
+
+# %% Source and Destination Port Analysis
+# =============================================================================
+# PORT ANALYSIS: EPHEMERAL vs REGISTERED PORTS
+# =============================================================================
+# Port Classification:
+# - Ephemeral ports (49152-65535): Dynamically assigned for outbound connections
+# - Registered ports (1024-49151): Assigned to specific services
+# - Well-known ports (0-1023): Reserved for common services (HTTP, SSH, etc.)
+#
+# Binary encoding: 1 = ephemeral port, 0 = registered/well-known port
+
+print("\n" + "=" * 60)
+print("PORT ANALYSIS")
+print("=" * 60)
+
+# Source Port Classification
 col_name = "Source Port ephemeral"
-## create boolean value for ephemeral and assigned ports
-"""
-    ephemeral port > 49151 = 1 
-    assigned/registered port <= 49151 = 0
-"""
-bully = df[ col_name ] > 49151
-df.loc[ bully , col_name ] = 1
-df.loc[ ~ bully , col_name ] = 0
-print( df[ col_name ].value_counts())
-# piechart_col( col_name )
-crosstab_col( col_name , "Attack Type" , "sourceport" , "attacktype" )
-
-# Destination Port
+print(f"\n--- {col_name} ---")
+print("Converting to binary: 1 = ephemeral (>49151), 0 = registered/well-known")
+bully = df[col_name] > 49151
+df.loc[bully, col_name] = 1
+df.loc[~bully, col_name] = 0
+print(df[col_name].value_counts())
+crosstab_col(col_name, "Attack Type", "sourceport", "attacktype")
+
+# Destination Port Classification
 col_name = "Destination Port ephemeral"
-## create boolean value for ephemeral and assigned ports
-"""
-    ephemeral port > 49151 = 1 
-    assigned/registered port <= 49151 = 0
-"""
-bully = df[ col_name ] > 49151
-df.loc[ bully , col_name ] = 1
-df.loc[ ~ bully , col_name ] = 0
-print( df[ col_name ].value_counts())
-# piechart_col( col_name )
-crosstab_col( col_name , "Attack Type" , "destport" , "attacktype" )
-
-# Protocol
+print(f"\n--- {col_name} ---")
+print("Converting to binary: 1 = ephemeral (>49151), 0 = registered/well-known")
+bully = df[col_name] > 49151
+df.loc[bully, col_name] = 1
+df.loc[~bully, col_name] = 0
+print(df[col_name].value_counts())
+crosstab_col(col_name, "Attack Type", "destport", "attacktype")
+
+# =============================================================================
+# PROTOCOL ANALYSIS
+# =============================================================================
+# Network protocols observed in the traffic:
+# - TCP: Connection-oriented, reliable delivery (web, email, file transfer)
+# - UDP: Connectionless, fast but unreliable (DNS, streaming, gaming)
+# - ICMP: Control messages and diagnostics (ping, traceroute)
+#
+# Binary encoding creates indicator columns for each protocol
+
+print("\n" + "=" * 60)
+print("PROTOCOL ANALYSIS")
+print("=" * 60)
+
 col_name = "Protocol"
-"""
-    UDP = { 1 if Protocol = "UDP" , 0 otherwise }
-    TCP = { 1 if Protocol = "TCP" , 0 otherwise }
-    IMCP = [ 0 , 0 ]
-"""
-print( df[ col_name ].value_counts())
-piechart_col( col_name )
-df = catvar_mapping( col_name , [ "UDP" , "ICMP" ])
-### cross table Protocol x Attack Type
-crosstab_col( col_name , "Attack Type" , col_name , "attacktype" )
-
-# Packet length
-fig = px.histogram( df , "Packet Length" )
+print(df[col_name].value_counts())
+piechart_col(col_name)
+
+# Create binary indicator columns for protocols
+if "Protocol UDP" not in df.columns:
+    df = catvar_mapping(col_name, ["UDP", "ICMP"])
+crosstab_col(col_name, "Attack Type", col_name, "attacktype")
+
+# =============================================================================
+# PACKET LENGTH DISTRIBUTION
+# =============================================================================
+# Packet length can indicate:
+# - Small packets: Control messages, ACKs, probes
+# - Medium packets: Typical web traffic
+# - Large packets: File transfers, streaming data
+# - Unusual sizes: Potential attack indicators
+
+print("\n" + "=" * 60)
+print("PACKET LENGTH ANALYSIS")
+print("=" * 60)
+
+fig = px.histogram(
+    df,
+    x="Packet Length",
+    title="Distribution of Network Packet Sizes",
+    labels={"Packet Length": "Packet Length (bytes)", "count": "Frequency"},
+    color_discrete_sequence=["#00CC96"]
+)
+fig.update_layout(
+    xaxis_title="Packet Length (bytes)",
+    yaxis_title="Number of Packets",
+    showlegend=False
+)
 fig.show()
 
-# Packet Type
+# =============================================================================
+# PACKET TYPE ANALYSIS
+# =============================================================================
+# Packet Types:
+# - Control packets: Network management (SYN, ACK, FIN, RST)
+# - Data packets: Actual payload/content transmission
+#
+# Binary encoding: Control = 1, Data = 0
+
+print("\n" + "=" * 60)
+print("PACKET TYPE ANALYSIS")
+print("=" * 60)
+
 col_name = "Packet Type"
-"""
-    Control = 1
-    Data = 0
-"""
-print( df[ col_name ].value_counts())
-df = catvar_mapping( col_name , [ "Control" ] , [ "Control" ])
-piechart_col( "Packet Type Control" )
+if col_name in df.columns:
+    print(df[col_name].value_counts())
+    df = catvar_mapping(col_name, ["Control"], ["Control"])
+    piechart_col("Packet Type Control")
+elif "Packet Type Control" in df.columns:
+    print("Packet Type already transformed to Packet Type Control")
+    piechart_col("Packet Type Control")
+
+# =============================================================================
+# TRAFFIC TYPE ANALYSIS
+# =============================================================================
+# Application layer protocols:
+# - DNS: Domain Name System queries/responses (port 53)
+# - HTTP: Web traffic (port 80)
+# - FTP: File Transfer Protocol (ports 20, 21)
+#
+# Binary encoding creates indicator columns for each traffic type
+
+print("\n" + "=" * 60)
+print("TRAFFIC TYPE ANALYSIS")
+print("=" * 60)
 
-# Traffic Type
 col_name = "Traffic Type"
-"""
-    DNS = { 1 if Traffic Type = "DNS" , 0 otherwise }
-    HTTP = { 1 if Traffic Type = "HTTP" , 0 otherwise }
-    FTP = [ 0 , 0 ]
-"""
-print( df[ col_name ].value_counts())
-df = catvar_mapping( col_name , [ "DNS" , "HTTP" ])
-piechart_col( col_name )
+print(df[col_name].value_counts())
+if "Traffic Type DNS" not in df.columns:
+    df = catvar_mapping(col_name, ["DNS", "HTTP"])
+piechart_col(col_name)
+
+# Malware Indicators
+# =============================================================================
+# SECURITY INDICATORS ANALYSIS
+# =============================================================================
+# These columns contain security-related flags and detection results
+
+print("\n" + "=" * 60)
+print("SECURITY INDICATORS ANALYSIS")
+print("=" * 60)
 
+# -----------------------------------------------------------------------------
 # Malware Indicators
+# Indicators of Compromise (IoC) detected by security systems
+# IoC examples: malicious file hashes, suspicious domains, known attack patterns
+# Binary encoding: 1 = IoC detected, 0 = No IoC detected
+# -----------------------------------------------------------------------------
 col_name = "Malware Indicators"
-print( df[ col_name ].value_counts())
-"""
-    IoC Detected = 1
-    pd.NA = 0
-"""
-df = catvar_mapping( col_name , [ "IoC Detected" ] , [ "/" ])
-piechart_col( col_name )
+print(f"\n--- {col_name} ---")
+print(df[col_name].value_counts())
 
+if df[col_name].dtype == 'object' or "IoC Detected" in df[col_name].astype(str).values:
+    df = catvar_mapping(col_name, ["IoC Detected"], ["/"])
+piechart_col(col_name, names=["No IoC Detected", "IoC Detected"])
+
+# -----------------------------------------------------------------------------
 # Anomaly Scores
-fig = px.histogram( df , "Anomaly Scores" )
+# Numerical score indicating how anomalous/suspicious the traffic is
+# Higher scores suggest more deviation from normal behavior
+# -----------------------------------------------------------------------------
+print("\n--- Anomaly Scores ---")
+fig = px.histogram(
+    df,
+    x="Anomaly Scores",
+    title="Distribution of Anomaly Scores",
+    labels={"Anomaly Scores": "Anomaly Score", "count": "Frequency"},
+    color_discrete_sequence=["#EF553B"]
+)
+fig.update_layout(
+    xaxis_title="Anomaly Score (higher = more suspicious)",
+    yaxis_title="Number of Records",
+    showlegend=False
+)
 fig.show()
 
+# -----------------------------------------------------------------------------
 # Alert Trigger
+# Whether the traffic triggered a security alert
+# Binary encoding: 1 = Alert triggered, 0 = No alert
+# -----------------------------------------------------------------------------
+print("\n--- Alert Trigger ---")
 col_name = "Alert Trigger"
-# print( df[ col_name ].value_counts())
-df = catvar_mapping( col_name , [ "Alert Triggered" ] , [ "/" ])
-piechart_col( col_name , names = [ "Alert triggered" , "Alert not triggered" ])
+if df[col_name].dtype == 'object' or "Alert Triggered" in df[col_name].astype(str).values:
+    df = catvar_mapping(col_name, ["Alert Triggered"], ["/"])
+piechart_col(col_name, names=["No Alert Triggered", "Alert Triggered"])
 
+# -----------------------------------------------------------------------------
 # Attack Signature
+# Pattern matching results from signature-based detection systems
+# Known Pattern A vs Known Pattern B (different attack signatures)
+# Binary encoding: Pattern A = 1, Pattern B = 0
+# -----------------------------------------------------------------------------
+print("\n--- Attack Signature ---")
 col_name = "Attack Signature"
-print( df[ "Attack Signature" ].value_counts())
-"""
-    Pattern A = 1
-    Pattern B = 0
-"""
-df = catvar_mapping( col_name , [ "Known Pattern A" ] , [ "patA" ])
-piechart_col( "Attack Signature patA" , [ "Pattern A" , "Pattern B" ])
-
-# Action taken
+if col_name in df.columns:
+    print(df["Attack Signature"].value_counts())
+    df = catvar_mapping(col_name, ["Known Pattern A"], ["patA"])
+    piechart_col("Attack Signature patA", ["Known Pattern A", "Known Pattern B"])
+elif "Attack Signature patA" in df.columns:
+    print("Attack Signature already transformed to Attack Signature patA")
+    piechart_col("Attack Signature patA", ["Known Pattern A", "Known Pattern B"])
+
+# -----------------------------------------------------------------------------
+# Action Taken
+# Response action by the security system
+# - Logged: Traffic recorded for analysis
+# - Blocked: Traffic denied/dropped
+# - Ignored: No action taken
+# -----------------------------------------------------------------------------
+print("\n--- Action Taken ---")
 col_name = "Action Taken"
-print( df[ col_name ].value_counts())
-df = catvar_mapping( col_name , [ "Logged" , "Blocked" ])
-piechart_col( col_name )
-
+if col_name in df.columns:
+    print(df[col_name].value_counts())
+    df = catvar_mapping(col_name, ["Logged", "Blocked"])
+    piechart_col(col_name)
+elif "Action Taken Logged" in df.columns:
+    print("Action Taken already transformed")
+
+# -----------------------------------------------------------------------------
 # Severity Level
+# Classification of threat severity
+# Ordinal encoding: Low = -1, Medium = 0, High = +1
+# This preserves the natural ordering for analysis
+# -----------------------------------------------------------------------------
+print("\n--- Severity Level ---")
 col_name = "Severity Level"
-print( df[ col_name ].value_counts())
-"""
-    Low = - 1
-    Medium = 0
-    High = + 1
-"""
-df.loc[ df[ col_name ] == "Low" , col_name ] = - 1
-df.loc[ df[ col_name ] == "Medium" , col_name ] = 0
-df.loc[ df[ col_name ] == "High" , col_name ] = + 1
-piechart_col( col_name , [ "Low" , "Medium" , "High" ])
+print(df[col_name].value_counts())
+
+df.loc[df[col_name] == "Low", col_name] = -1
+df.loc[df[col_name] == "Medium", col_name] = 0
+df.loc[df[col_name] == "High", col_name] = +1
+piechart_col(col_name, ["Low Severity", "Medium Severity", "High Severity"])
+
+# %% [markdown]
+# ## Device and User-Agent Analysis
+# This section parses User-Agent strings to extract:
+# - Browser information (family, version)
+# - Operating system details
+# - Device characteristics (mobile, tablet, PC, bot)
+
+# %% Device Information Extraction
+# =============================================================================
+# USER-AGENT PARSING AND DEVICE CLASSIFICATION
+# =============================================================================
+# The "Device Information" column contains User-Agent strings that reveal:
+# - Browser: Chrome, Firefox, Safari, Edge, IE, etc.
+# - Operating System: Windows, macOS, Linux, iOS, Android
+# - Device Type: Desktop PC, Mobile phone, Tablet
+# - Bot Detection: Automated crawlers vs human users
+#
+# This information helps identify:
+# - Attack vectors targeting specific platforms
+# - Bot-driven attacks vs human-initiated threats
+# - Vulnerable browser/OS combinations
+
+print("\n" + "=" * 60)
+print("DEVICE INFORMATION ANALYSIS")
+print("=" * 60)
 
-#%% Device Information
 col_name = "Device Information"
-print( df[ col_name ].value_counts())
-col = df.columns.get_loc( col_name )
-df.insert( col + 1 , "Browser family" , value = pd.NA )
-df.insert( col + 2 , "Browser major" , value = pd.NA )
-df.insert( col + 3 , "Browser minor" , value = pd.NA )
-df.insert( col + 4 , "OS family" , value = pd.NA )
-df.insert( col + 5 , "OS major" , value = pd.NA )
-df.insert( col + 6 , "OS minor" , value = pd.NA )
-df.insert( col + 7 , "Device family" , value = pd.NA )
-df.insert( col + 8 , "Device brand" , value = pd.NA )
-df.insert( col + 9 , "Device type" , value = pd.NA )
-df.insert( col + 10 , "Device bot" , value = pd.NA )
-
-df[ "Browser family" ] = df[ col_name ].apply( lambda x : parse( x ).browser.family if parse( x ).browser.family is not None else pd.NA )
-df[ "Browser major" ] = df[ col_name ].apply( lambda x : parse( x ).browser.version[ 0 ] if parse( x ).browser.version[ 0 ] is not None else pd.NA )
-df[ "Browser minor" ] = df[ col_name ].apply( lambda x: parse( x ).browser.version[ 1 ] if parse( x ).browser.version[ 1 ] is not None else pd.NA )
-df[ "OS family" ] = df[ col_name ].apply( lambda x : parse( x ).os.family if parse( x ).os.family is not None else pd.NA )
-df[ "OS major" ] = df[ col_name ].apply( lambda x : parse( x ).os.version[ 0 ] if len( parse( x ).os.version ) > 0 and parse( x ).os.version[ 0 ] is not None else pd.NA )
-df[ "OS minor" ] = df[ col_name ].apply( lambda x : parse( x ).os.version[ 1 ] if len( parse( x ).os.version ) > 1 and parse( x ).os.version[ 1 ] is not None else pd.NA )
-df[ "OS patch" ] = df[ col_name ].apply( lambda x : parse( x ).os.version[ 2 ] if len( parse( x ).os.version ) > 2 and parse( x ).os.version[ 2 ] is not None else pd.NA )
-df[ "Device family" ] = df[ col_name ].apply (lambda x : parse( x ).device.family if parse( x ).device.family is not None else pd.NA )
-df[ "Device brand" ] = df[ col_name].apply( lambda x : parse( x ).device.brand if parse( x ).device.brand is not None else pd.NA )
-# do not agree with setting to not specified , why nnot leave it pd.NA ??
-# df[ "OS major" ] = df[ "OS_major" ].fillna( "not specified" )
-# df[ "OS minor" ] = df[ "OS_major" ].fillna( "not specified" )
-# df[ "OS patch" ] = df[ "OS_patch" ].fillna( "not specified" )
-# df[ "Device brand" ] = df[ "Device_brand" ].fillna( "not specified" )
-# device info
-def Device_type( ua_string ) :
-    try :
-        if not ua_string or pd.isna( ua_string ) :
+print(f"Unique User-Agent strings: {df[col_name].nunique()}")
+print("\nTop 10 User-Agent strings:")
+print(df[col_name].value_counts().head(10))
+
+# Parse User-Agent strings (skip if already processed)
+if "Browser family" not in df.columns:
+    print("\nParsing User-Agent strings...")
+    col = df.columns.get_loc(col_name)
+
+    # Create columns for parsed device information
+    device_info_cols = [
+        "Browser family", "Browser major", "Browser minor",
+        "OS family", "OS major", "OS minor",
+        "Device family", "Device brand", "Device type", "Device bot"
+    ]
+    for i, new_col in enumerate(device_info_cols, start=1):
+        if new_col not in df.columns:
+            df.insert(col + i, new_col, value=pd.NA)
+
+    # Extract browser information
+    df["Browser family"] = df[col_name].apply(
+        lambda x: parse(x).browser.family if parse(x).browser.family is not None else pd.NA
+    )
+    df["Browser major"] = df[col_name].apply(
+        lambda x: parse(x).browser.version[0]
+        if parse(x).browser.version[0] is not None
+        else pd.NA
+    )
+    df["Browser minor"] = df[col_name].apply(
+        lambda x: parse(x).browser.version[1]
+        if parse(x).browser.version[1] is not None
+        else pd.NA
+    )
+
+    # Extract operating system information
+    df["OS family"] = df[col_name].apply(
+        lambda x: parse(x).os.family if parse(x).os.family is not None else pd.NA
+    )
+    df["OS major"] = df[col_name].apply(
+        lambda x: parse(x).os.version[0]
+        if len(parse(x).os.version) > 0 and parse(x).os.version[0] is not None
+        else pd.NA
+    )
+    df["OS minor"] = df[col_name].apply(
+        lambda x: parse(x).os.version[1]
+        if len(parse(x).os.version) > 1 and parse(x).os.version[1] is not None
+        else pd.NA
+    )
+    df["OS patch"] = df[col_name].apply(
+        lambda x: parse(x).os.version[2]
+        if len(parse(x).os.version) > 2 and parse(x).os.version[2] is not None
+        else pd.NA
+    )
+
+    # Extract device information
+    df["Device family"] = df[col_name].apply(
+        lambda x: parse(x).device.family if parse(x).device.family is not None else pd.NA
+    )
+    df["Device brand"] = df[col_name].apply(
+        lambda x: parse(x).device.brand if parse(x).device.brand is not None else pd.NA
+    )
+    print("User-Agent parsing complete.")
+else:
+    print("Device information already extracted - skipping parsing.")
+
+# Note: Keeping NA values for missing information is preferred over
+# placeholder strings like "not specified" for proper statistical analysis
+
+# -----------------------------------------------------------------------------
+# Device Type Classification
+# Classify devices as Mobile, Tablet, or PC based on User-Agent
+# -----------------------------------------------------------------------------
+def Device_type(ua_string):
+    """
+    Classify device type from User-Agent string.
+
+    Returns:
+        str: "Mobile", "Tablet", "PC", or pd.NA if unknown
+    """
+    try:
+        if not ua_string or pd.isna(ua_string):
             return pd.NA
-        ua = ua_parse( ua_string )
-        if getattr( ua , "is_mobile" , False ) :
+        ua = ua_parse(ua_string)
+        if getattr(ua, "is_mobile", False):
             return "Mobile"
-        if getattr( ua , "is_tablet" , False ) :
+        if getattr(ua, "is_tablet", False):
             return "Tablet"
-        if getattr( ua , "is_pc", False ) :
+        if getattr(ua, "is_pc", False):
             return "PC"
-        return pd.NA # replaced "Unknown" with pd.NA
-    except :
-        return pd.NA # replaced "Unknown" with pd.NA
-df[ "Device type" ] = df[ col_name ].apply( Device_type())
-# detection of bots
-df[ "Device bot" ] = df[ col_name ].apply( lambda x: ua_parse( x ).is_bot )
-
-#%% Network Segment
+        return pd.NA
+    except:
+        return pd.NA
+
+
+# Classify device types and detect bots
+if "Device type" not in df.columns or df["Device type"].isna().all():
+    print("Classifying device types...")
+    df["Device type"] = df[col_name].apply(Device_type)
+
+# Bot detection - identifies automated crawlers and scripts
+if "Device bot" not in df.columns or df["Device bot"].isna().all():
+    print("Detecting bot traffic...")
+    df["Device bot"] = df[col_name].apply(lambda x: ua_parse(x).is_bot)
+
+# %% [markdown]
+# ## Network Infrastructure Analysis
+# This section analyzes network segmentation and security system logs
+
+# %% Network Segment and Security Logs
+# =============================================================================
+# NETWORK SEGMENTATION ANALYSIS
+# =============================================================================
+# Network segments represent logical divisions of the network:
+# - Segment A, B, C: Different network zones (e.g., DMZ, internal, guest)
+# - Segmentation helps contain breaches and control traffic flow
+#
+# Binary encoding: segA = 1 if Segment A, segB = 1 if Segment B, else [0,0] for C
+
+print("\n" + "=" * 60)
+print("NETWORK SEGMENTATION ANALYSIS")
+print("=" * 60)
+
 col_name = "Network Segment"
-print( df[ col_name ].value_counts())
-"""
-    segA = { 1 if "Segment A" ; 0 otherwise }
-    segB = { 1 if "Segment B" ; 0 otherwise }
-    "Segment C" = [ 0 , 0 ]
-"""
-df = catvar_mapping( col_name , [ "Segment A" , "Segment B" ] , [ "segA" , "segB" ]) 
-piechart_col( col_name )
+print(df[col_name].value_counts())
+
+if "Network Segment segA" not in df.columns:
+    df = catvar_mapping(col_name, ["Segment A", "Segment B"], ["segA", "segB"])
+piechart_col(col_name)
+
+# =============================================================================
+# GEO-LOCATION DATA PARSING
+# =============================================================================
+# Parse "City, State" format into separate columns for geographic analysis
+
+print("\n" + "=" * 60)
+print("GEO-LOCATION DATA PARSING")
+print("=" * 60)
 
-# Geo-location Data
 col_name = "Geo-location Data"
-print( df[ col_name ].value_counts())
-col = df.columns.get_loc( col_name )
-df.insert( col + 1 , "Geo-location City" , value = pd.NA )
-df.insert( col + 2 , "Geo-location State" , value = pd.NA )
-def geolocation_data( info ) :
-    city , state = info.split( ", " )
-    return pd.Series([ city , state ])
-df[[ "Geo-location City" , "Geo-location State" ]] = df[ "Geo-location Data" ].apply( lambda x : geolocation_data( x ))
+print(f"Unique locations: {df[col_name].nunique()}")
+print("\nTop 10 locations:")
+print(df[col_name].value_counts().head(10))
+
+
+def geolocation_data(info):
+    """Split 'City, State' string into separate components."""
+    city, state = info.split(", ")
+    return pd.Series([city, state])
+
 
+if "Geo-location City" not in df.columns:
+    print("Parsing geo-location data...")
+    col = df.columns.get_loc(col_name)
+    df.insert(col + 1, "Geo-location City", value=pd.NA)
+    df.insert(col + 2, "Geo-location State", value=pd.NA)
+    df[["Geo-location City", "Geo-location State"]] = df["Geo-location Data"].apply(
+        lambda x: geolocation_data(x)
+    )
+
+# =============================================================================
+# SECURITY SYSTEM LOGS ANALYSIS
+# =============================================================================
+# Firewall Logs: Records of traffic allowed/denied by firewall rules
+# IDS/IPS Alerts: Intrusion Detection/Prevention System alerts
+# Log Source: Where the log originated (Firewall vs Server)
+
+print("\n" + "=" * 60)
+print("SECURITY SYSTEM LOGS ANALYSIS")
+print("=" * 60)
+
+# -----------------------------------------------------------------------------
 # Firewall Logs
+# Binary: 1 = Log data present, 0 = No log data
+# -----------------------------------------------------------------------------
+print("\n--- Firewall Logs ---")
 col_name = "Firewall Logs"
-print( df[ col_name ].value_counts())
-"""
-    Log Data = 1
-    pd.NA = 0
-"""
-df = catvar_mapping( col_name , [ "Log Data" ] , [ "/" ])
-# piechart_col( col_name , [ "Log Data" , "No Log Data" ])
+print(df[col_name].value_counts())
 
+if df[col_name].dtype == 'object' or "Log Data" in df[col_name].astype(str).values:
+    df = catvar_mapping(col_name, ["Log Data"], ["/"])
+
+# -----------------------------------------------------------------------------
 # IDS/IPS Alerts
+# Intrusion Detection/Prevention System alerts
+# Binary: 1 = Alert data present, 0 = No alert
+# -----------------------------------------------------------------------------
+print("\n--- IDS/IPS Alerts ---")
 col_name = "IDS/IPS Alerts"
-print( df[ col_name ].value_counts())
-"""
-    Alert Data = 1
-    pd.NA = 0
-"""
-bully = df[ col_name ] == "Alert Data"
-df = catvar_mapping( col_name , [ "Alert Data" ] , [ "/" ])
-# piechart_col( col_name , [ "Alert Data" , "No Alert Data" ])
+print(df[col_name].value_counts())
+# Binary: 1 = Alert data present, 0 = No alert
+if df[col_name].dtype == 'object' or "Alert Data" in df[col_name].astype(str).values:
+    df = catvar_mapping(col_name, ["Alert Data"], ["/"])
 
+# -----------------------------------------------------------------------------
 # Log Source
+# Origin of the log entry
+# Binary: Firewall = 1, Server = 0
+# -----------------------------------------------------------------------------
+print("\n--- Log Source ---")
 col_name = "Log Source"
-print( df[ col_name ].value_counts())
-"""
-    Firewall = 1
-    Server = 0
-"""
-df = catvar_mapping( col_name , [ "Firewall" ] , [ "Firewall" ])
-# piechart_col( col_name , [ "Firewall" , "Server" ])
-crosstab_col( "Log Source Firewall" , "Firewall Logs" , "logsource" , "firewallogs" )
+if col_name in df.columns:
+    print(df[col_name].value_counts())
+    df = catvar_mapping(col_name, ["Firewall"], ["Firewall"])
+elif "Log Source Firewall" in df.columns:
+    print("Log Source already transformed to Log Source Firewall")
 
+# Generate cross-tabulation between log source and firewall logs
+crosstab_col("Log Source Firewall", "Firewall Logs", "logsource", "firewallogs")
 
-#%% seperat dfs for attack types
 
-df_attype = {}
-attypes = [ "Malware" , "Intrusion" , "DDoS" ]
-for attype in attypes :
-    df_attype[ attype ] = df[ df[ "Attack Type"] == attype ]
+# %% [markdown]
+# ## Attack Type Segmentation
+# Create separate DataFrames for each attack type to enable
+# comparative analysis between Malware, Intrusion, and DDoS attacks
 
+# %% Separate DataFrames by Attack Type
+# =============================================================================
+# ATTACK TYPE SEGMENTATION
+# =============================================================================
+# Split dataset by attack type for comparative visualizations
+# This enables side-by-side analysis of attack characteristics
 
-#%% geo plot of "Attack Type" by "Anomaly Score"
+print("\n" + "=" * 60)
+print("ATTACK TYPE SEGMENTATION")
+print("=" * 60)
+
+df_attype = {}
+attypes = ["Malware", "Intrusion", "DDoS"]
+for attype in attypes:
+    df_attype[attype] = df[df["Attack Type"] == attype]
+    print(f"{attype}: {len(df_attype[attype]):,} records")
+
+
+# %% [markdown]
+# ## Geographic Distribution by Attack Type and Anomaly Score
+# Visualize where different attack types originate and target,
+# with color intensity representing the anomaly score
+
+# %% Geographic Plot: Attack Types with Anomaly Scores
+# =============================================================================
+# GEO-VISUALIZATION: ATTACK TYPES BY ANOMALY SCORE
+# =============================================================================
+# 6-panel geographic visualization:
+# - Rows: Malware, Intrusion, DDoS attacks
+# - Columns: Source IPs (left) and Destination IPs (right)
+# - Color: Anomaly score (Viridis scale - dark=low, bright=high)
+
+print("\n" + "=" * 60)
+print("GEOGRAPHIC ATTACK VISUALIZATION")
+print("=" * 60)
 
 fig = subp(
-    rows = 3 ,
-    cols = 2 ,
-    specs = [
-        [{ "type" : "scattergeo" } , { "type" : "scattergeo" }] , 
-        [{ "type" : "scattergeo" } , { "type" : "scattergeo" }] ,
-        [{ "type" : "scattergeo" } , { "type" : "scattergeo" }] ,
-        ] ,
-    subplot_titles = (
-        "Source IP locations" ,
-        "Destination IP locations" 
-        )
-    )
-for i , ( attype , symb ) in enumerate( zip( attypes , [ "diamond" , "diamond" , "diamond" ])) :
+    rows=3,
+    cols=2,
+    specs=[
+        [{"type": "scattergeo"}, {"type": "scattergeo"}],
+        [{"type": "scattergeo"}, {"type": "scattergeo"}],
+        [{"type": "scattergeo"}, {"type": "scattergeo"}],
+    ],
+    subplot_titles=(
+        "Malware - Source IPs", "Malware - Destination IPs",
+        "Intrusion - Source IPs", "Intrusion - Destination IPs",
+        "DDoS - Source IPs", "DDoS - Destination IPs"
+    ),
+)
+
+for i, (attype, symb) in enumerate(zip(attypes, ["diamond", "diamond", "diamond"])):
+    # Source IP locations (left column)
     fig.add_trace(
         go.Scattergeo(
-            lat = df_attype[ attype ][ "Source IP latitude" ] ,
-            lon = df_attype[ attype ][ "Source IP longitude" ] ,
-            mode = "markers" ,
-            marker = {
-                "size" : 4 ,
-                "symbol" : symb ,
-                "color" : df_attype[ attype ][ "Anomaly Scores" ] ,
-                "colorscale" : "Viridis" ,
-                "cmin" : df_attype[ attype ][ "Anomaly Scores" ].min() ,
-                "cmax" : df_attype[ attype ][ "Anomaly Scores" ].max() ,
-                "colorbar" : { "title" : "Anomaly Score" }
-                }
-            ) ,
-        row = i + 1 , 
-        col = 1
-        )
+            lat=df_attype[attype]["Source IP latitude"],
+            lon=df_attype[attype]["Source IP longitude"],
+            mode="markers",
+            name=f"{attype} Source",
+            marker={
+                "size": 4,
+                "symbol": symb,
+                "color": df_attype[attype]["Anomaly Scores"],
+                "colorscale": "Viridis",
+                "cmin": df_attype[attype]["Anomaly Scores"].min(),
+                "cmax": df_attype[attype]["Anomaly Scores"].max(),
+                "colorbar": {"title": "Anomaly<br>Score", "len": 0.25, "y": 0.85 - i * 0.33},
+            },
+            hovertemplate=f"<b>{attype} Attack Origin</b><br>Lat: %{{lat:.2f}}<br>Lon: %{{lon:.2f}}<br>Anomaly Score: %{{marker.color:.2f}}<extra></extra>"
+        ),
+        row=i + 1,
+        col=1,
+    )
+    # Destination IP locations (right column)
     fig.add_trace(
         go.Scattergeo(
-            lat = df_attype[ attype ][ "Destination IP latitude" ] ,
-            lon = df_attype[ attype ][ "Destination IP longitude" ] ,
-            mode = "markers" ,
-            marker = {
-                "size" : 4 ,
-                "symbol" : symb ,
-                "color" : df_attype[ attype ][ "Anomaly Scores" ] ,
-                "colorscale" : "Viridis" ,
-                "cmin" : df_attype[ attype ][ "Anomaly Scores" ].min() ,
-                "cmax" : df_attype[ attype ][ "Anomaly Scores" ].max() ,
-                "colorbar" : { "title" : "Anomaly Score" }
-                }
-            ) ,
-        row = i + 1 , 
-        col = 2
-        )
-    i = i + 1
+            lat=df_attype[attype]["Destination IP latitude"],
+            lon=df_attype[attype]["Destination IP longitude"],
+            mode="markers",
+            name=f"{attype} Dest",
+            marker={
+                "size": 4,
+                "symbol": symb,
+                "color": df_attype[attype]["Anomaly Scores"],
+                "colorscale": "Viridis",
+                "cmin": df_attype[attype]["Anomaly Scores"].min(),
+                "cmax": df_attype[attype]["Anomaly Scores"].max(),
+                "colorbar": {"title": "Anomaly<br>Score", "len": 0.25, "y": 0.85 - i * 0.33, "x": 1.02},
+                "showscale": False,  # Hide duplicate colorbar
+            },
+            hovertemplate=f"<b>{attype} Attack Target</b><br>Lat: %{{lat:.2f}}<br>Lon: %{{lon:.2f}}<br>Anomaly Score: %{{marker.color:.2f}}<extra></extra>"
+        ),
+        row=i + 1,
+        col=2,
+    )
+
 fig.update_geos(
-    # projection_type = "orthographic" ,
-    showcountries = True ,
-    showland = True ,
-    # landcolor = "LightGreen"
+    showcountries=True,
+    showland=True,
+    landcolor="lightgray",
+    countrycolor="white"
 )
 fig.update_layout(
-    height = 4000 ,
-    margin = { 
-        "r" : 0 , 
-        "t" : 10 , 
-        "l" : 0 , 
-        "b" : 0 
-        } ,
-    title_text = "IP Address Locations" ,
-    title_x = 0.5
+    height=1200,
+    margin={"r": 50, "t": 80, "l": 0, "b": 0},
+    title_text="Geographic Distribution of Attacks by Type and Anomaly Score",
+    title_x=0.5,
+    title_font_size=18,
+    showlegend=False,
 )
 fig.show()
 
 
-#%% Packet Length x Attack Types
+# %% [markdown]
+# ## Attack Characteristics Comparison
+# Compare packet lengths and anomaly scores across different attack types
+
+# %% Packet Length by Attack Type
+# =============================================================================
+# PACKET LENGTH DISTRIBUTION BY ATTACK TYPE
+# =============================================================================
+# Strip plot showing packet length distribution for each attack type
+# Helps identify if different attacks have characteristic packet sizes
+
+print("\n" + "=" * 60)
+print("PACKET LENGTH BY ATTACK TYPE")
+print("=" * 60)
 
 fig = go.Figure()
 
-for i , ( attype , symb ) in enumerate( zip( attypes , [ "diamond" , "diamond" , "diamond" ])) :
+# Color palette for attack types
+colors = {"Malware": "#636EFA", "Intrusion": "#EF553B", "DDoS": "#00CC96"}
+
+for i, (attype, symb) in enumerate(zip(attypes, ["diamond", "diamond", "diamond"])):
     fig.add_trace(
         go.Scatter(
-            x = df_attype[ attype ][ "Packet Length" ] ,
-            y = [ i ] * df_attype[ attype ].shape[ 0 ] ,
-            mode = "markers" ,
-            name = attype ,
-            marker = {
-                "size" : 4 ,
-                "symbol" : symb ,
-                "opacity" : 0.1
-                }
-                ) ,
+            x=df_attype[attype]["Packet Length"],
+            y=[i] * df_attype[attype].shape[0],
+            mode="markers",
+            name=f"{attype} ({len(df_attype[attype]):,} records)",
+            marker={
+                "size": 4,
+                "symbol": symb,
+                "opacity": 0.15,
+                "color": colors[attype]
+            },
+            hovertemplate=f"<b>{attype}</b><br>Packet Length: %{{x}} bytes<extra></extra>"
+        ),
+    )
 
-        )
-    i = i + 1
+fig.update_layout(
+    title="Packet Length Distribution by Attack Type",
+    title_font_size=16,
+    xaxis_title="Packet Length (bytes)",
+    yaxis=dict(
+        tickmode='array',
+        tickvals=[0, 1, 2],
+        ticktext=attypes,
+        title="Attack Type"
+    ),
+    height=400,
+    showlegend=True,
+    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5)
+)
 fig.show()
 
-# Anomaly Scores x Attack Types
+# =============================================================================
+# ANOMALY SCORE DISTRIBUTION BY ATTACK TYPE
+# =============================================================================
+# Strip plot showing anomaly score distribution for each attack type
+# Higher anomaly scores indicate more suspicious/unusual traffic patterns
+
+print("\n" + "=" * 60)
+print("ANOMALY SCORES BY ATTACK TYPE")
+print("=" * 60)
 
 fig = go.Figure()
 
-for i , ( attype , symb ) in enumerate( zip( attypes , [ "diamond" , "diamond" , "diamond" ])) :
+for i, (attype, symb) in enumerate(zip(attypes, ["diamond", "diamond", "diamond"])):
     fig.add_trace(
         go.Scatter(
-            x = df_attype[ attype ][ "Anomaly Scores" ] ,
-            y = [ i ] * df_attype[ attype ].shape[ 0 ] ,
-            mode = "markers" ,
-            name = attype ,
-            marker = {
-                "size" : 4 ,
-                "symbol" : symb ,
-                "opacity" : 0.1
-                }
-                ) ,
+            x=df_attype[attype]["Anomaly Scores"],
+            y=[i] * df_attype[attype].shape[0],
+            mode="markers",
+            name=f"{attype} ({len(df_attype[attype]):,} records)",
+            marker={
+                "size": 4,
+                "symbol": symb,
+                "opacity": 0.15,
+                "color": colors[attype]
+            },
+            hovertemplate=f"<b>{attype}</b><br>Anomaly Score: %{{x:.2f}}<extra></extra>"
+        ),
+    )
 
-        )
-    i = i + 1
+fig.update_layout(
+    title="Anomaly Score Distribution by Attack Type",
+    title_font_size=16,
+    xaxis_title="Anomaly Score (higher = more suspicious)",
+    yaxis=dict(
+        tickmode='array',
+        tickvals=[0, 1, 2],
+        ticktext=attypes,
+        title="Attack Type"
+    ),
+    height=400,
+    showlegend=True,
+    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5)
+)
 fig.show()
 
-#%% general crosstable
-
-def sankey_diag( cols_bully = [
-        True , # "Source IP country"
-        True , # "Destination IP country"
-        True , # "Source Port ephemeral"
-        True , # "Destination Port ephemeral"
-        True , # "Protocol"
-        True , # "Packet Type Control"
-        True , # "Traffic Type"
-        True , # "Malware Indicators"
-        True , # "Alert Trigger"
-        True , # "Attack Signature patA"
-        True , # "Action Taken"
-        True , # "Severity Level"
-        True , # "Network Segment"
-        True , # "Firewall Logs"
-        True , # "IDS/IPS Alerts"
-        True , # "Log Source Firewall"
-        ] , ntop = 10 ) :
-    cols = np.array([
-        "Source IP country" ,
-        "Destination IP country" ,
-        "Source Port ephemeral" ,
-        "Destination Port ephemeral" ,
-        "Protocol" , 
-        "Packet Type Control" ,
-        "Traffic Type" ,
-        "Malware Indicators" ,
-        "Alert Trigger" ,
-        "Attack Signature patA" ,
-        "Action Taken" ,
-        "Severity Level" ,
-        "Network Segment" ,
-        "Firewall Logs" ,
-        "IDS/IPS Alerts" ,
-        "Log Source Firewall"
-        ])
-    cols = cols[ np.array( cols_bully )]
-    
-    idx_ct =  []
+# %% [markdown]
+# ## Multi-Variable Sankey Diagram
+# Visualize the flow of traffic characteristics leading to different attack types
+# This helps identify which combinations of features are associated with each attack
+
+# %% General Cross-Table Sankey Diagram
+# =============================================================================
+# MULTI-VARIABLE SANKEY DIAGRAM GENERATOR
+# =============================================================================
+# Creates a Sankey diagram showing how categorical variable values
+# flow toward different attack types. Useful for understanding
+# which feature combinations characterize each attack type.
+
+
+def sankey_diag(
+    cols_bully=[
+        True,   # "Source IP country"
+        True,   # "Destination IP country"
+        True,   # "Source Port ephemeral"
+        True,   # "Destination Port ephemeral"
+        True,   # "Protocol"
+        True,   # "Packet Type Control"
+        True,   # "Traffic Type"
+        True,   # "Malware Indicators"
+        True,   # "Alert Trigger"
+        True,   # "Attack Signature patA"
+        True,   # "Action Taken"
+        True,   # "Severity Level"
+        True,   # "Network Segment"
+        True,   # "Firewall Logs"
+        True,   # "IDS/IPS Alerts"
+        True,   # "Log Source Firewall"
+    ],
+    ntop=10,
+):
+    """
+    Generate a Sankey diagram showing feature value flows to attack types.
+
+    Parameters:
+    -----------
+    cols_bully : list of bool
+        Boolean flags indicating which columns to include in the diagram
+    ntop : int
+        Number of top countries to show (others grouped as "other")
+
+    Returns:
+    --------
+    DataFrame
+        Cross-tabulation percentages used to build the diagram
+    """
+    cols = np.array(
+        [
+            "Source IP country",
+            "Destination IP country",
+            "Source Port ephemeral",
+            "Destination Port ephemeral",
+            "Protocol",
+            "Packet Type Control",
+            "Traffic Type",
+            "Malware Indicators",
+            "Alert Trigger",
+            "Attack Signature patA",
+            "Action Taken",
+            "Severity Level",
+            "Network Segment",
+            "Firewall Logs",
+            "IDS/IPS Alerts",
+            "Log Source Firewall",
+        ]
+    )
+    cols = cols[np.array(cols_bully)]
+
+    idx_ct = []
     labels = []
-    if "Source IP country" in cols :
-        SIP = df[ "Source IP country" ].copy( deep = True )
+    if "Source IP country" in cols:
+        SIP = df["Source IP country"].copy(deep=True)
         SIP.name = "SIP"
-        SIPlabs = pd.Series( SIP.value_counts().index[ : ntop ])
-        bully = ( SIP.isin( SIPlabs ) | SIP.isna())
-        SIP.loc[ ~ bully ] = "other"
-        SIPlabs = "SIP " + pd.concat([ SIPlabs , pd.Series([ "other" ])])
-        labels.extend( SIPlabs.to_list())
-        idx_ct = idx_ct + [ SIP ]
-        cols = cols[ cols != "Source IP country" ]
-    if "Destination IP country" in cols :
-        DIP = df[ "Destination IP country" ].copy( deep = True )
+        SIPlabs = pd.Series(SIP.value_counts().index[:ntop])
+        bully = SIP.isin(SIPlabs) | SIP.isna()
+        SIP.loc[~bully] = "other"
+        SIPlabs = "SIP " + pd.concat([SIPlabs, pd.Series(["other"])])
+        labels.extend(SIPlabs.to_list())
+        idx_ct = idx_ct + [SIP]
+        cols = cols[cols != "Source IP country"]
+    if "Destination IP country" in cols:
+        DIP = df["Destination IP country"].copy(deep=True)
         DIP.name = "DIP"
-        DIPlabs = pd.Series( DIP.value_counts().index[ : ntop ])
-        bully = ( DIP.isin( DIPlabs ) | DIP.isna())
-        DIP.loc[ ~ bully ] = "other"
-        DIPlabs = "DIP " + pd.concat([ DIPlabs , pd.Series([ "other" ])])
-        labels.extend( DIPlabs.to_list())
-        idx_ct = idx_ct + [ DIP ]
-        cols = cols[ cols != "Destination IP country" ]
-        
+        DIPlabs = pd.Series(DIP.value_counts().index[:ntop])
+        bully = DIP.isin(DIPlabs) | DIP.isna()
+        DIP.loc[~bully] = "other"
+        DIPlabs = "DIP " + pd.concat([DIPlabs, pd.Series(["other"])])
+        labels.extend(DIPlabs.to_list())
+        idx_ct = idx_ct + [DIP]
+        cols = cols[cols != "Destination IP country"]
+
     # build cross table with Attack Type in columns and multi-index of variables in index
-    idx_ct = idx_ct + [ df[ col ] for col in cols ]
-    print( idx_ct )
-    crosstabs = pd.crosstab( 
-        index = idx_ct ,
-        columns = df[ "Attack Type" ]
-        )
-    
+    idx_ct = idx_ct + [df[col] for col in cols]
+    print(idx_ct)
+    crosstabs = pd.crosstab(index=idx_ct, columns=df["Attack Type"])
+
     # compute labels
-    for c in np.append( cols , "Attack Type" ) :
-        vals = df[ c ].unique()
-        for v in vals :
-            labels.append( f"{ c } { v }" )
+    for c in np.append(cols, "Attack Type"):
+        vals = df[c].unique()
+        for v in vals:
+            labels.append(f"{c} {v}")
     # computation of source , target , value
     source = []
     target = []
     value = []
     nlvl = crosstabs.index.nlevels
-    for idx , row in crosstabs.iterrows()  :
+    for idx, row in crosstabs.iterrows():
         row_labs = []
-        if ( nlvl == 1 ) :
-            row_labs.append( f"{ crosstabs.index.name } { idx }" )
-        else :
-            for i , val in enumerate( idx ) :
-                row_labs.append( f"{ crosstabs.index.names[ i ] } { val }" )
-        for attype in crosstabs.columns :
-            val = row[ attype ]
-            for i in range( 0 , nlvl - 1 ) :
-                source.append( labels.index( row_labs[ i ]))
-                target.append( labels.index( row_labs[ i + 1 ]))
-                value.append( val )
-            source.append( labels.index( row_labs[ - 1 ]))
-            target.append( labels.index( f"Attack Type { attype }" ))
-            value.append( val )
-    
-    # plot the sankey diagram
-    n = len( labels )
+        if nlvl == 1:
+            row_labs.append(f"{crosstabs.index.name} {idx}")
+        else:
+            for i, val in enumerate(idx):
+                row_labs.append(f"{crosstabs.index.names[i]} {val}")
+        for attype in crosstabs.columns:
+            val = row[attype]
+            for i in range(0, nlvl - 1):
+                source.append(labels.index(row_labs[i]))
+                target.append(labels.index(row_labs[i + 1]))
+                value.append(val)
+            source.append(labels.index(row_labs[-1]))
+            target.append(labels.index(f"Attack Type {attype}"))
+            value.append(val)
+
+    # Plot the Sankey diagram with Inferno color scale
+    n = len(labels)
     colors = px.colors.sample_colorscale(
-        px.colors.sequential.Inferno ,
-        [ i / ( n - 1 ) for i in range( n )]
-        ) 
-    fig = go.Figure( data = [ go.Sankey(
-        node = dict(
-          pad = 15 ,
-          thickness = 20 ,
-          line = dict( 
-              color = "rgba( 0 , 0 , 0 , 0.1 )" ,
-              width = 0.5 
-              ) ,
-          label = labels ,
-          color = colors ,
-          ) ,
-        link = dict(
-          source = source ,
-          target = target ,
-          value = value 
-          ))])
+        px.colors.sequential.Inferno, [i / (n - 1) for i in range(n)]
+    )
+    fig = go.Figure(
+        data=[
+            go.Sankey(
+                node=dict(
+                    pad=15,
+                    thickness=20,
+                    line=dict(color="rgba(0, 0, 0, 0.1)", width=0.5),
+                    label=labels,
+                    color=colors,
+                    hovertemplate="<b>%{label}</b><br>Total: %{value}<extra></extra>"
+                ),
+                link=dict(
+                    source=source,
+                    target=target,
+                    value=value,
+                    hovertemplate="Flow from %{source.label} to %{target.label}<br>Count: %{value}<extra></extra>"
+                ),
+            )
+        ]
+    )
     fig.update_layout(
-        title_text = "Sankey Diagram" ,
-        # font_family = "Courier New" ,
-        # font_color = "blue" , 
-        font_size = 20 ,
-        title_font_family = "Avenir" ,
-        title_font_color = "black",
-        )
+        title_text="Feature Value Flow to Attack Types",
+        title_font_size=18,
+        font_size=14,
+        title_font_family="Avenir",
+        title_font_color="black",
+    )
     fig.show()
-    
+
+    # Return normalized cross-tabulation (percentages)
     crosstabs = crosstabs / crosstabs.sum().sum() * 100
-    
+
     return crosstabs
 
-crosstabs = sankey_diag([
-    False , # "Source IP country"
-    False , # "Destination IP country"
-    False , # "Source Port ephemeral"
-    False , # "Destination Port ephemeral"
-    True , # "Protocol"
-    False , # "Packet Type Control"
-    True , # "Traffic Type"
-    True , # "Malware Indicators"
-    False , # "Alert Trigger"
-    False , # "Attack Signature patA"
-    False , # "Action Taken"
-    False , # "Severity Level"
-    False , # "Network Segment"
-    False , # "Firewall Logs"
-    True , # "IDS/IPS Alerts"
-    False , # "Log Source Firewall"
-    ])
-
-
-#%%
-
-
-def paracat_diag( cols_bully = [
-        True , # "Source IP country"
-        True , # "Destination IP country"
-        True , # "Source Port ephemeral"
-        True , # "Destination Port ephemeral"
-        True , # "Protocol"
-        True , # "Packet Type Control"
-        True , # "Traffic Type"
-        True , # "Malware Indicators"
-        True , # "Alert Trigger"
-        True , # "Attack Signature patA"
-        True , # "Action Taken"
-        True , # "Severity Level"
-        True , # "Network Segment"
-        True , # "Firewall Logs"
-        True , # "IDS/IPS Alerts"
-        True , # "Log Source Firewall"
-        ] , 
-        colorvar = "Attack Type" ,
-        ntop = 10 ,
-        ) :
-    
-    cols = np.array([
-        "Source IP country" ,
-        "Destination IP country" ,
-        "Source Port ephemeral" ,
-        "Destination Port ephemeral" ,
-        "Protocol" , 
-        "Packet Type Control" ,
-        "Traffic Type" ,
-        "Malware Indicators" ,
-        "Alert Trigger" ,
-        "Attack Signature patA" ,
-        "Action Taken" ,
-        "Severity Level" ,
-        "Network Segment" ,
-        "Firewall Logs" ,
-        "IDS/IPS Alerts" ,
-        "Log Source Firewall"
-        ])
-    cols = cols[ np.array( cols_bully )]
-    
+
+# Generate Sankey diagram with selected features
+# Selected: Protocol, Traffic Type, Malware Indicators, IDS/IPS Alerts
+print("\n" + "=" * 60)
+print("MULTI-VARIABLE SANKEY ANALYSIS")
+print("=" * 60)
+print("Features included: Protocol, Traffic Type, Malware Indicators, IDS/IPS Alerts")
+
+crosstabs = sankey_diag(
+    [
+        False,  # "Source IP country"
+        False,  # "Destination IP country"
+        False,  # "Source Port ephemeral"
+        False,  # "Destination Port ephemeral"
+        True,   # "Protocol" - INCLUDED
+        False,  # "Packet Type Control"
+        True,   # "Traffic Type" - INCLUDED
+        True,   # "Malware Indicators" - INCLUDED
+        False,  # "Alert Trigger"
+        False,  # "Attack Signature patA"
+        False,  # "Action Taken"
+        False,  # "Severity Level"
+        False,  # "Network Segment"
+        False,  # "Firewall Logs"
+        True,   # "IDS/IPS Alerts" - INCLUDED
+        False,  # "Log Source Firewall"
+    ]
+)
+
+
+# %% [markdown]
+# ## Parallel Categories Diagram
+# Interactive visualization showing relationships between categorical
+# variables and attack types. Lines connect category values, with
+# color indicating the selected variable's values.
+
+# %% Parallel Categories Diagram
+# =============================================================================
+# PARALLEL CATEGORIES DIAGRAM
+# =============================================================================
+# Creates an interactive parallel categories plot showing how
+# different categorical feature values relate to attack types.
+# The diagram allows exploration of multi-dimensional categorical relationships.
+
+
+def paracat_diag(
+    cols_bully=[
+        True,  # "Source IP country"
+        True,  # "Destination IP country"
+        True,  # "Source Port ephemeral"
+        True,  # "Destination Port ephemeral"
+        True,  # "Protocol"
+        True,  # "Packet Type Control"
+        True,  # "Traffic Type"
+        True,  # "Malware Indicators"
+        True,  # "Alert Trigger"
+        True,  # "Attack Signature patA"
+        True,  # "Action Taken"
+        True,  # "Severity Level"
+        True,  # "Network Segment"
+        True,  # "Firewall Logs"
+        True,  # "IDS/IPS Alerts"
+        True,  # "Log Source Firewall"
+    ],
+    colorvar="Attack Type",
+    ntop=10,
+):
+    cols = np.array(
+        [
+            "Source IP country",
+            "Destination IP country",
+            "Source Port ephemeral",
+            "Destination Port ephemeral",
+            "Protocol",
+            "Packet Type Control",
+            "Traffic Type",
+            "Malware Indicators",
+            "Alert Trigger",
+            "Attack Signature patA",
+            "Action Taken",
+            "Severity Level",
+            "Network Segment",
+            "Firewall Logs",
+            "IDS/IPS Alerts",
+            "Log Source Firewall",
+        ]
+    )
+    cols = cols[np.array(cols_bully)]
+
     dims_var = {}
-    if "Source IP country" in cols :
-        SIP = df[ "Source IP country" ].copy( deep = True )
+    if "Source IP country" in cols:
+        SIP = df["Source IP country"].copy(deep=True)
         SIP.name = "SIP"
-        SIPlabs = pd.Series( SIP.value_counts().index[ : ntop ])
-        bully = ( SIP.isin( SIPlabs ) | SIP.isna())
-        SIP.loc[ ~ bully ] = "other"
-        SIPlabs = "SIP " + pd.concat([ SIPlabs , pd.Series([ "other" ])])
-        cols = cols[ cols != "Source IP country" ]
-        dims_var[ "SIP" ] = go.parcats.Dimension(
-            values = SIP ,
+        SIPlabs = pd.Series(SIP.value_counts().index[:ntop])
+        bully = SIP.isin(SIPlabs) | SIP.isna()
+        SIP.loc[~bully] = "other"
+        SIPlabs = "SIP " + pd.concat([SIPlabs, pd.Series(["other"])])
+        cols = cols[cols != "Source IP country"]
+        dims_var["SIP"] = go.parcats.Dimension(
+            values=SIP,
             # categoryorder = "category ascending" ,
-            label = "Source IP country"
-            )
-        cols = np.append( cols , "SIP" )
-    if "Destination IP country" in cols :
-        DIP = df[ "Destination IP country" ].copy( deep = True )
+            label="Source IP country",
+        )
+        cols = np.append(cols, "SIP")
+    if "Destination IP country" in cols:
+        DIP = df["Destination IP country"].copy(deep=True)
         DIP.name = "DIP"
-        DIPlabs = pd.Series( DIP.value_counts().index[ : ntop ])
-        bully = ( DIP.isin( DIPlabs ) | DIP.isna())
-        DIP.loc[ ~ bully ] = "other"
-        DIPlabs = "DIP " + pd.concat([ DIPlabs , pd.Series([ "other" ])])
-        cols = cols[ cols != "Destination IP country" ]
-        dims_var[ "DIP" ] = go.parcats.Dimension(
-            values = DIP ,
+        DIPlabs = pd.Series(DIP.value_counts().index[:ntop])
+        bully = DIP.isin(DIPlabs) | DIP.isna()
+        DIP.loc[~bully] = "other"
+        DIPlabs = "DIP " + pd.concat([DIPlabs, pd.Series(["other"])])
+        cols = cols[cols != "Destination IP country"]
+        dims_var["DIP"] = go.parcats.Dimension(
+            values=DIP,
             # categoryorder = "category ascending" ,
-            label = "Destination IP country"
-            )
-        cols = np.append( cols , "DIP" )
-    for col in cols[( cols != "SIP" ) & ( cols != "DIP" )] :
-        print( col )
-        dims_var[ col ] = go.parcats.Dimension(
-            values = df[ col ] ,
+            label="Destination IP country",
+        )
+        cols = np.append(cols, "DIP")
+    for col in cols[(cols != "SIP") & (cols != "DIP")]:
+        print(col)
+        dims_var[col] = go.parcats.Dimension(
+            values=df[col],
             # categoryorder = "category ascending" ,
-            label = col
-            )
-    
+            label=col,
+        )
+
     dim_attypes = go.parcats.Dimension(
-        values = df[ "Attack Type" ], 
-        label = "Attack Type", 
+        values=df["Attack Type"],
+        label="Attack Type",
         # categoryarray = [ "Malware" , "Intrusion" , "DDoS" ] ,
-        ticktext = [ "Malware" , "Intrusion" , "DDoS" ]
-        )
-    
-    dims = [ dims_var[ col ] for col in cols ]
-    dims.append( dim_attypes )
-    
-    if colorvar == "SIP" :
+        ticktext=["Malware", "Intrusion", "DDoS"],
+    )
+
+    dims = [dims_var[col] for col in cols]
+    dims.append(dim_attypes)
+
+    if colorvar == "SIP":
         colorvar = SIP
-    elif colorvar == "DIP" :
+    elif colorvar == "DIP":
         colorvar = DIP
-    elif ( colorvar in cols ) or ( colorvar == "Attack Type" ) :
-        colorvar = df[ colorvar ]
-    else :
-        ValueError( "colorvar must be in cols" )
+    elif (colorvar in cols) or (colorvar == "Attack Type"):
+        colorvar = df[colorvar]
+    else:
+        ValueError("colorvar must be in cols")
     catcolor = colorvar.unique()
-    catcolor_n = catcolor.shape[ 0 ]
-    color = colorvar.map({ cat : i for i , cat in enumerate( catcolor )})
-    positions = [ i / ( catcolor_n - 1 ) if ( catcolor_n > 1 ) else 0 for i in range( 0 , catcolor_n )]
+    catcolor_n = catcolor.shape[0]
+    color = colorvar.map({cat: i for i, cat in enumerate(catcolor)})
+    positions = [
+        i / (catcolor_n - 1) if (catcolor_n > 1) else 0 for i in range(0, catcolor_n)
+    ]
     palette = px.colors.sequential.Viridis
-    colors = [ px.colors.sample_colorscale( palette , p )[ 0 ] for p in positions ]
-    colorscale = [[ positions[ i ] , colors[ i ]] for i in range( 0 , catcolor_n )]
-    
-    fig = go.Figure( data = [ go.Parcats( 
-        dimensions = dims ,
-        line = { 
-            "color" : color ,
-            "colorscale" : colorscale ,
-            "shape" : "hspline" ,
-            } ,
-        hoveron = "color" ,
-        hoverinfo = "count+probability" ,
-        labelfont = { "size" : 18 , "family" : "Times" } ,
-        tickfont = { "size" : 16 , "family" : "Times" } ,
-        arrangement = "freeform" ,
-        )])
+    colors = [px.colors.sample_colorscale(palette, p)[0] for p in positions]
+    colorscale = [[positions[i], colors[i]] for i in range(0, catcolor_n)]
+
+    fig = go.Figure(
+        data=[
+            go.Parcats(
+                dimensions=dims,
+                line={
+                    "color": color,
+                    "colorscale": colorscale,
+                    "shape": "hspline",
+                },
+                hoveron="color",
+                hoverinfo="count+probability",
+                labelfont={"size": 18, "family": "Times"},
+                tickfont={"size": 16, "family": "Times"},
+                arrangement="freeform",
+            )
+        ]
+    )
     fig.show()
 
-paracat_diag([
-    False , # "Source IP country"
-    False , # "Destination IP country"
-    False , # "Source Port ephemeral"
-    False , # "Destination Port ephemeral"
-    True , # "Protocol"
-    False , # "Packet Type Control"
-    True , # "Traffic Type"
-    False , # "Malware Indicators"
-    False , # "Alert Trigger"
-    True , # "Attack Signature patA"
-    False , # "Action Taken"
-    True , # "Severity Level"
-    True , # "Network Segment"
-    False , # "Firewall Logs"
-    False , # "IDS/IPS Alerts"
-    False , # "Log Source Firewall"
-    ] ,
-    colorvar = "Attack Signature patA" ,
-    )
 
+# Generate parallel categories diagram
+# Selected features: Protocol, Traffic Type, Attack Signature, Severity Level, Network Segment
+# Color: Attack Signature Pattern A (to see how signature types flow through other features)
+print("\n" + "=" * 60)
+print("PARALLEL CATEGORIES DIAGRAM")
+print("=" * 60)
+print("Features: Protocol, Traffic Type, Attack Signature, Severity Level, Network Segment")
+print("Color coding: Attack Signature Pattern (A vs B)")
+
+paracat_diag(
+    [
+        False,  # "Source IP country"
+        False,  # "Destination IP country"
+        False,  # "Source Port ephemeral"
+        False,  # "Destination Port ephemeral"
+        True,   # "Protocol" - INCLUDED
+        False,  # "Packet Type Control"
+        True,   # "Traffic Type" - INCLUDED
+        False,  # "Malware Indicators"
+        False,  # "Alert Trigger"
+        True,   # "Attack Signature patA" - INCLUDED
+        False,  # "Action Taken"
+        True,   # "Severity Level" - INCLUDED
+        True,   # "Network Segment" - INCLUDED
+        False,  # "Firewall Logs"
+        False,  # "IDS/IPS Alerts"
+        False,  # "Log Source Firewall"
+    ],
+    colorvar="Attack Signature patA",
+)
+
+
+# %% [markdown]
+# ## Statistical Analysis: Correlation and Chi-Square Tests
+# Measure the strength of association between categorical variables
+# and the target variable (Attack Type)
+
+# %% Matthews Correlation Coefficients
+# =============================================================================
+# MATTHEWS CORRELATION COEFFICIENT (MCC) ANALYSIS
+# =============================================================================
+# The Matthews Correlation Coefficient (MCC) measures the quality of
+# binary classifications. Values range from -1 to +1:
+# - +1: Perfect prediction
+# -  0: No better than random
+# - -1: Total disagreement
+#
+# For multi-class, this generalizes to the Phi coefficient.
+
+print("\n" + "=" * 60)
+print("MATTHEWS CORRELATION COEFFICIENTS")
+print("=" * 60)
+print("Measuring correlation between each feature and Attack Type")
+print("(Values close to 0 indicate no linear correlation)\n")
+
+
+def catvar_corr(col, target="Attack Type"):
+    """Calculate Matthews correlation coefficient between a feature and target."""
+    corr = matthews_corrcoef(df[target], df[col].astype(str))
+    print(f"  {col:35} : {corr:+.4f}")
+
+
+catvars = np.array(
+    [
+        "Source IP country",
+        "Destination IP country",
+        "Source Port ephemeral",
+        "Destination Port ephemeral",
+        "Protocol",
+        "Packet Type Control",
+        "Traffic Type",
+        "Malware Indicators",
+        "Alert Trigger",
+        "Attack Signature patA",
+        "Action Taken",
+        "Severity Level",
+        "Network Segment",
+        "Firewall Logs",
+        "IDS/IPS Alerts",
+        "Log Source Firewall",
+    ]
+)
+for c in catvars:
+    catvar_corr(c)
+
+
+# %% [markdown]
+# ## Chi-Square Test of Independence
+# Test whether categorical variables are independent of Attack Type
 
-#%% coefficients computation
-
-# matthews corrcoef
-
-def catvar_corr( col , target = "Attack Type") :
-    corr = matthews_corrcoef( df[ target ], df[ col ].astype( str ))
-    print( f"phi corr between { target } and { col } = { corr }" )
-catvars = np.array([
-    "Source IP country" ,
-    "Destination IP country" ,
-    "Source Port ephemeral" ,
-    "Destination Port ephemeral" ,
-    "Protocol" , 
-    "Packet Type Control" ,
-    "Traffic Type" ,
-    "Malware Indicators" ,
-    "Alert Trigger" ,
-    "Attack Signature patA" ,
-    "Action Taken" ,
-    "Severity Level" ,
-    "Network Segment" ,
-    "Firewall Logs" ,
-    "IDS/IPS Alerts" ,
-    "Log Source Firewall"
-    ])
-for c in catvars :
-    catvar_corr( c )
-
-#%% chi 2
+# %% Chi-Square Independence Test
+# =============================================================================
+# CHI-SQUARE TEST OF INDEPENDENCE
+# =============================================================================
+# Tests whether there is a significant association between Protocol and Attack Type
+# - H0: Variables are independent (no association)
+# - H1: Variables are not independent (association exists)
+#
+# If p-value < 0.05, we reject H0 and conclude significant association
+
+print("\n" + "=" * 60)
+print("CHI-SQUARE TEST: Protocol vs Attack Type")
+print("=" * 60)
 
 from scipy.stats import chi2_contingency
-# obs = np.array([[10, 10, 20], [20, 20, 20]])
-df_catvar = df[[
-    "Attack Type",
-    "Source Port ephemeral" ,
-    "Destination Port ephemeral" ,
-    "Protocol" , 
-    "Packet Type Control" ,
-    "Traffic Type" ,
-    "Malware Indicators" ,
-    "Alert Trigger" ,
-    "Attack Signature patA" ,
-    "Action Taken" ,
-    "Severity Level" ,
-    "Network Segment" ,
-    "Firewall Logs" ,
-    "IDS/IPS Alerts" ,
-    "Log Source Firewall"
-    ]]
-res = chi2_contingency( df_catvar.values.T[ 1 : ].astype( str ))
-print( res.statistic )
-print( res.pvalue )
-print( res.dof )
-print( res.expected_freq )
-
-#%% mca
 
+# Prepare categorical variables for analysis
+df_catvar = df[
+    [
+        "Attack Type",
+        "Source Port ephemeral",
+        "Destination Port ephemeral",
+        "Protocol",
+        "Packet Type Control",
+        "Traffic Type",
+        "Malware Indicators",
+        "Alert Trigger",
+        "Attack Signature patA",
+        "Action Taken",
+        "Severity Level",
+        "Network Segment",
+        "Firewall Logs",
+        "IDS/IPS Alerts",
+        "Log Source Firewall",
+    ]
+]
+
+# Encode categorical columns as integers for contingency table
+df_catvar_encoded = df_catvar.copy()
+for col in df_catvar_encoded.columns:
+    if df_catvar_encoded[col].dtype == 'object':
+        df_catvar_encoded[col] = pd.Categorical(df_catvar_encoded[col]).codes
+
+# Perform chi-square test
+res = chi2_contingency(pd.crosstab(df_catvar_encoded['Attack Type'], df_catvar_encoded['Protocol']))
+print(f"Chi-square statistic: {res.statistic:.4f}")
+print(f"P-value: {res.pvalue:.6f}")
+print(f"Degrees of freedom: {res.dof}")
+print(f"\nExpected frequencies:")
+print(res.expected_freq)
+
+if res.pvalue < 0.05:
+    print("\n=> Significant association detected (p < 0.05)")
+else:
+    print("\n=> No significant association (p >= 0.05)")
+
+
+# %% [markdown]
+# ## Multiple Correspondence Analysis (MCA)
+# Dimensionality reduction technique for categorical variables
+# Similar to PCA but for categorical data
+
+# %% Multiple Correspondence Analysis
+# =============================================================================
+# MULTIPLE CORRESPONDENCE ANALYSIS (MCA)
+# =============================================================================
+# MCA is a data analysis technique for nominal categorical data,
+# used to detect and represent underlying structures in a data set.
+# It's the categorical equivalent of Principal Component Analysis (PCA).
+#
+# Key outputs:
+# - Eigenvalues: Amount of variance explained by each component
+# - Component coordinates: Position of category levels in reduced space
+
+print("\n" + "=" * 60)
+print("MULTIPLE CORRESPONDENCE ANALYSIS (MCA)")
+print("=" * 60)
+
+# Initialize MCA with 3 components
 mca = prince.MCA(
     n_components=3,
     n_iter=3,
     copy=True,
     check_input=True,
-    engine='sklearn',
-    random_state=42
+    engine="sklearn",
+    random_state=42,
 )
 
-df_catvar = df[[
-    "Attack Type",
-    "Source Port ephemeral" ,
-    "Destination Port ephemeral" ,
-    "Protocol" , 
-    "Packet Type Control" ,
-    "Traffic Type" ,
-    "Malware Indicators" ,
-    "Alert Trigger" ,
-    "Attack Signature patA" ,
-    "Action Taken" ,
-    "Severity Level" ,
-    "Network Segment" ,
-    "Firewall Logs" ,
-    "IDS/IPS Alerts" ,
-    "Log Source Firewall"
-    ]]
+# Select categorical variables for MCA
+df_catvar = df[
+    [
+        "Attack Type",
+        "Source Port ephemeral",
+        "Destination Port ephemeral",
+        "Protocol",
+        "Packet Type Control",
+        "Traffic Type",
+        "Malware Indicators",
+        "Alert Trigger",
+        "Attack Signature patA",
+        "Action Taken",
+        "Severity Level",
+        "Network Segment",
+        "Firewall Logs",
+        "IDS/IPS Alerts",
+        "Log Source Firewall",
+    ]
+]
+
+# Fit MCA model
 mca = mca.fit(df_catvar)
 
+# Alternative MCA approaches for comparison
 one_hot = pd.get_dummies(df_catvar)
 
-mca_no_one_hot = prince.MCA(one_hot=False)
-mca_no_one_hot = mca_no_one_hot.fit(one_hot)
-mca_without_correction = prince.MCA(correction=None)
-
-mca_with_benzecri_correction = prince.MCA(correction='benzecri')
-mca_with_greenacre_correction = prince.MCA(correction='greenacre')
+# MCA with one_hot=False requires positive values (add 1 to avoid zeros)
+one_hot_positive = one_hot + 1
 
-mca.eigenvalues_summary
+mca_no_one_hot = prince.MCA(one_hot=False)
+mca_no_one_hot = mca_no_one_hot.fit(one_hot_positive)
 
-#%% SARIMA analysis on Attack type
+# Different correction methods for eigenvalue adjustment
+mca_without_correction = prince.MCA(correction=None)
+mca_with_benzecri_correction = prince.MCA(correction="benzecri")
+mca_with_greenacre_correction = prince.MCA(correction="greenacre")
+
+# Display MCA results
+print("\nMCA Eigenvalues Summary:")
+print("(Shows variance explained by each component)")
+print(mca.eigenvalues_summary)
+
+
+# %% [markdown]
+# ## Time Series Analysis: Attack Patterns Over Time
+# Analyze temporal patterns in attack occurrences using:
+# - Daily attack counts by type
+# - Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)
+# - Rolling averages for trend detection
+
+# %% Time Series Analysis - Daily Attack Counts
+# =============================================================================
+# TIME SERIES ANALYSIS: DAILY ATTACK PATTERNS
+# =============================================================================
+# Aggregate attacks by day and analyze temporal patterns
+# This helps identify:
+# - Seasonal patterns (weekly, monthly cycles)
+# - Trend changes over time
+# - Anomalous periods with unusual attack volumes
+
+print("\n" + "=" * 60)
+print("TIME SERIES ANALYSIS: DAILY ATTACK COUNTS")
+print("=" * 60)
+
+# Aggregate attacks by day
+Attacks_pday = df.copy(deep=True)
+Attacks_pday["date_dd"] = Attacks_pday["date"].dt.floor("d")
+Attacks_pday = (
+    Attacks_pday.groupby(["date_dd", "Attack Type"]).size().unstack().iloc[1:-1,]
+)
 
-Attacks_pday = df.copy( deep = True )
-Attacks_pday[ "date_dd" ] = Attacks_pday[ "date" ].dt.floor( "d" )
-Attacks_pday = Attacks_pday.groupby([ "date_dd" , "Attack Type" ]).size().unstack().iloc[ 1 : - 1 , ]
+print(f"Time series length: {len(Attacks_pday)} days")
+print(f"Date range: {Attacks_pday.index.min()} to {Attacks_pday.index.max()}")
 
-# plot n attacks per day
+# -----------------------------------------------------------------------------
+# Daily Attack Count Time Series Plot
+# Shows raw daily counts for each attack type
+# -----------------------------------------------------------------------------
 fig = subp(
-    rows = 3 ,
-    cols = 1 ,
-    subplot_titles = (
-        "Malware" ,
-        "Intrusion" ,
-        "DDos" ,
-        )
-    )
+    rows=3,
+    cols=1,
+    subplot_titles=(
+        "Malware Attacks per Day",
+        "Intrusion Attempts per Day",
+        "DDoS Attacks per Day",
+    ),
+    vertical_spacing=0.08
+)
+
+# Malware time series
 fig.add_trace(
     go.Scatter(
-        x = Attacks_pday.index ,
-        y = Attacks_pday[ "Malware" ] ,
-    ) ,
-    row = 1 ,
-    col = 1 ,
-    )
+        x=Attacks_pday.index,
+        y=Attacks_pday["Malware"],
+        mode="lines",
+        name="Malware",
+        line=dict(color="#636EFA"),
+        hovertemplate="<b>Malware</b><br>Date: %{x}<br>Count: %{y}<extra></extra>"
+    ),
+    row=1,
+    col=1,
+)
+
+# Intrusion time series
 fig.add_trace(
     go.Scatter(
-        x = Attacks_pday.index ,
-        y = Attacks_pday[ "Intrusion" ] ,
-    ) ,
-    row = 2 ,
-    col = 1 ,
-    )
+        x=Attacks_pday.index,
+        y=Attacks_pday["Intrusion"],
+        mode="lines",
+        name="Intrusion",
+        line=dict(color="#EF553B"),
+        hovertemplate="<b>Intrusion</b><br>Date: %{x}<br>Count: %{y}<extra></extra>"
+    ),
+    row=2,
+    col=1,
+)
+
+# DDoS time series
 fig.add_trace(
     go.Scatter(
-        x = Attacks_pday.index ,
-        y = Attacks_pday[ "DDoS" ] ,
-    ) ,
-    row = 3 ,
-    col = 1 ,
-    )
+        x=Attacks_pday.index,
+        y=Attacks_pday["DDoS"],
+        mode="lines",
+        name="DDoS",
+        line=dict(color="#00CC96"),
+        hovertemplate="<b>DDoS</b><br>Date: %{x}<br>Count: %{y}<extra></extra>"
+    ),
+    row=3,
+    col=1,
+)
+
+fig.update_layout(
+    height=800,
+    title_text="Daily Attack Counts by Type",
+    title_font_size=18,
+    showlegend=True,
+    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5)
+)
+fig.update_xaxes(title_text="Date", row=3, col=1)
+fig.update_yaxes(title_text="Count")
+fig.show()
 
-# plot ACF & PACF
+# -----------------------------------------------------------------------------
+# ACF and PACF Analysis
+# Autocorrelation helps identify time series patterns:
+# - ACF: Correlation between observations at different lags
+# - PACF: Direct correlation at each lag, removing intermediate effects
+# These help determine ARIMA model parameters (p, d, q)
+# -----------------------------------------------------------------------------
+print("\n--- Autocorrelation Analysis ---")
 
 fig = subp(
-    rows = 3 ,
-    cols = 2 ,
-    subplot_titles = (
-        "Malware ACF" ,
-        "Malware PACF" ,
-        "Intrusion ACF" ,
-        "Intrusion PACF" ,
-        "DDoS ACF" ,
-        "DDoS PACF" ,
-        )
-    )
+    rows=3,
+    cols=2,
+    subplot_titles=(
+        "Malware - Autocorrelation (ACF)",
+        "Malware - Partial Autocorrelation (PACF)",
+        "Intrusion - Autocorrelation (ACF)",
+        "Intrusion - Partial Autocorrelation (PACF)",
+        "DDoS - Autocorrelation (ACF)",
+        "DDoS - Partial Autocorrelation (PACF)",
+    ),
+    vertical_spacing=0.1,
+    horizontal_spacing=0.08
+)
+
 from statsmodels.tsa.stattools import pacf
 from statsmodels.tsa.stattools import acf
-import plotly.graph_objects as go
 
 Attacktype_TSanalysis = {}
 nlags = 100
-for i , attacktype in enumerate([ "Malware" , "Intrustion" , "DDoS" ]) :
-    Attacktype_TSanalysis[ f"{ attacktype }_ACF" ] = acf( Attacks_pday[ "Malware" ] ,  nlags = nlags )
-    Attacktype_TSanalysis[ f"{ attacktype }_PACF" ] = pacf( Attacks_pday[ "Malware" ] ,  nlags = nlags )
-    fig.add_trace(
-        go.Scatter(
-            x = list( range( 0 , nlags + 1 )) , 
-            y = Attacktype_TSanalysis[ f"{ attacktype }_ACF" ] ,
-            mode = "lines" ,
-            name = "ACF" ,
-        ) ,
-        row = i + 1 ,
-        col = 1 ,
-        )
-    fig.add_trace(
-        go.Scatter(
-            x = list( range( 0 , nlags + 1 )) , 
-            y = Attacktype_TSanalysis[ f"{ attacktype }_PACF" ] ,
-            mode = "lines" ,
-            name = "PACF" ,
-        ) ,
-        row = i + 1,
-        col = 2 ,
-        )
-fig.show()
-
-#%%
-
-df.to_csv( "data/df.csv" , sep = "|" )
-
-
-
-
-
-
-
-
-
-
-
-
 
+# Color scheme for attack types
+ts_colors = {"Malware": "#636EFA", "Intrusion": "#EF553B", "DDoS": "#00CC96"}
 
+for i, attacktype in enumerate(["Malware", "Intrusion", "DDoS"]):
+    # Calculate ACF and PACF
+    Attacktype_TSanalysis[f"{attacktype}_ACF"] = acf(
+        Attacks_pday[attacktype], nlags=nlags
+    )
+    Attacktype_TSanalysis[f"{attacktype}_PACF"] = pacf(
+        Attacks_pday[attacktype], nlags=nlags
+    )
 
+    # Plot ACF
+    fig.add_trace(
+        go.Scatter(
+            x=list(range(0, nlags + 1)),
+            y=Attacktype_TSanalysis[f"{attacktype}_ACF"],
+            mode="lines",
+            name=f"{attacktype} ACF",
+            line=dict(color=ts_colors[attacktype]),
+            hovertemplate=f"<b>{attacktype} ACF</b><br>Lag: %{{x}}<br>Correlation: %{{y:.3f}}<extra></extra>"
+        ),
+        row=i + 1,
+        col=1,
+    )
 
+    # Plot PACF
+    fig.add_trace(
+        go.Scatter(
+            x=list(range(0, nlags + 1)),
+            y=Attacktype_TSanalysis[f"{attacktype}_PACF"],
+            mode="lines",
+            name=f"{attacktype} PACF",
+            line=dict(color=ts_colors[attacktype]),
+            hovertemplate=f"<b>{attacktype} PACF</b><br>Lag: %{{x}}<br>Correlation: %{{y:.3f}}<extra></extra>"
+        ),
+        row=i + 1,
+        col=2,
+    )
 
+fig.update_layout(
+    height=900,
+    title_text="Autocorrelation Analysis for Attack Time Series",
+    title_font_size=18,
+    showlegend=False
+)
+fig.update_xaxes(title_text="Lag (days)")
+fig.update_yaxes(title_text="Correlation")
+fig.show()
 
 
+# %% Save Processed Dataset
+# =============================================================================
+# SAVE PROCESSED DATASET
+# =============================================================================
+# Export the processed DataFrame with all engineered features
 
+print("\n" + "=" * 60)
+print("SAVING PROCESSED DATASET")
+print("=" * 60)
 
+df.to_csv("data/df.csv", sep="|")
+print("Dataset saved to data/df.csv")
+print(f"Total records: {len(df):,}")
+print(f"Total columns: {len(df.columns)}")
 
 
+# %% [markdown]
+# ## Rolling Average Trend Analysis
+# Smooth the daily time series using rolling averages to identify trends
 
+# %% Rolling Average Analysis
+# =============================================================================
+# ROLLING AVERAGE TREND ANALYSIS
+# =============================================================================
+# Apply a 30-day rolling average to smooth out daily fluctuations
+# and reveal underlying trends in attack patterns
 
-#%%
+print("\n" + "=" * 60)
+print("ROLLING AVERAGE TREND ANALYSIS")
+print("=" * 60)
 
-rw = 30
+rw = 30  # 30-day rolling window
+print(f"Using {rw}-day rolling average window")
 
 tr1 = go.Scatter(
-    x = Attacks_pday.index ,
-    y = Attacks_pday[ "Malware" ].rolling( rw ).mean() ,
-    line_color = "blue" ,
+    x=Attacks_pday.index,
+    y=Attacks_pday["Malware"].rolling(rw).mean(),
+    name=f"Malware ({rw}-day MA)",
+    line=dict(color="#636EFA", width=2),
+    hovertemplate="<b>Malware</b><br>Date: %{x}<br>30-day Avg: %{y:.1f}<extra></extra>"
 )
 tr2 = go.Scatter(
-    x = Attacks_pday.index ,
-    y = Attacks_pday[ "Intrusion" ].rolling( rw ).mean() ,
-    line_color = "red" ,
-    # yaxis = "y2"
+    x=Attacks_pday.index,
+    y=Attacks_pday["Intrusion"].rolling(rw).mean(),
+    name=f"Intrusion ({rw}-day MA)",
+    line=dict(color="#EF553B", width=2),
+    hovertemplate="<b>Intrusion</b><br>Date: %{x}<br>30-day Avg: %{y:.1f}<extra></extra>"
 )
 tr3 = go.Scatter(
-    x = Attacks_pday.index ,
-    y = Attacks_pday[ "DDoS" ].rolling( rw ).mean() ,
-    line_color = "#000000" ,
-    # yaxis = "y2" 
+    x=Attacks_pday.index,
+    y=Attacks_pday["DDoS"].rolling(rw).mean(),
+    name=f"DDoS ({rw}-day MA)",
+    line=dict(color="#00CC96", width=2),
+    hovertemplate="<b>DDoS</b><br>Date: %{x}<br>30-day Avg: %{y:.1f}<extra></extra>"
 )
 
+# Create combined rolling average plot
 fig = subp()
 fig.add_trace(tr1)
 fig.add_trace(tr2)
 fig.add_trace(tr3)
+fig.update_layout(
+    title_text=f"Attack Trends: {rw}-Day Rolling Average by Attack Type",
+    title_font_size=18,
+    xaxis_title="Date",
+    yaxis_title="Average Daily Attacks",
+    legend=dict(
+        orientation="h",
+        yanchor="bottom",
+        y=1.02,
+        xanchor="center",
+        x=0.5,
+        title="Attack Type"
+    ),
+    hovermode="x unified"
+)
 fig.show()
 
-#%%
 
-px.line(
-    Attacks_pday ,
-    x = Attacks_pday.index ,
-    y = Attacks_pday[ "DDoS" ].rolling( 15 ).mean() ,
-    mode = "line" ,
-    title = "Number of DDoS attacks MA per day"
-)
+# %% DDoS Short-Term Trend (15-day MA)
+# =============================================================================
+# DDoS SHORT-TERM TREND ANALYSIS
+# =============================================================================
+# 15-day rolling average for more responsive trend detection
+
+print("\n--- DDoS 15-Day Moving Average ---")
 
-#%%
-Attacks_pmonth = df.copy( deep = True )
-Attacks_pmonth[ "date_mm" ] = Attacks_pmonth[ "date" ].dt.ceil( "d" )
-Attacks_pmonth = Attacks_pmonth.groupby([ "date_mm" , "Attack Type" ]).size().unstack().iloc[ 1 : - 1 , ]
-px.line(
-    Attacks_pmonth ,
-    x = Attacks_pmonth.index ,
-    y = "DDoS" ,
-    title = "Number of DDoS attacks per month"
+fig = px.line(
+    Attacks_pday,
+    x=Attacks_pday.index,
+    y=Attacks_pday["DDoS"].rolling(15).mean(),
+    title="DDoS Attack Trend: 15-Day Rolling Average",
+    labels={"y": "15-Day Average Attacks", "x": "Date"}
+)
+fig.update_traces(
+    line=dict(color="#00CC96", width=2),
+    hovertemplate="<b>DDoS</b><br>Date: %{x}<br>15-day Avg: %{y:.1f}<extra></extra>"
+)
+fig.update_layout(
+    xaxis_title="Date",
+    yaxis_title="Average Daily DDoS Attacks",
+    showlegend=False
 )
+fig.show()
+
+
+# %% Monthly Attack Aggregation
+# =============================================================================
+# MONTHLY ATTACK AGGREGATION
+# =============================================================================
+# Aggregate attacks by month for longer-term pattern analysis
 
+print("\n" + "=" * 60)
+print("MONTHLY ATTACK ANALYSIS")
+print("=" * 60)
 
+Attacks_pmonth = df.copy(deep=True)
+Attacks_pmonth["date_mm"] = Attacks_pmonth["date"].dt.ceil("d")
+Attacks_pmonth = (
+    Attacks_pmonth.groupby(["date_mm", "Attack Type"]).size().unstack().iloc[1:-1,]
+)
+
+fig = px.line(
+    Attacks_pmonth,
+    x=Attacks_pmonth.index,
+    y="DDoS",
+    title="Monthly DDoS Attack Volume",
+    labels={"y": "Number of Attacks", "date_mm": "Date"}
+)
+fig.update_traces(
+    line=dict(color="#00CC96", width=2),
+    hovertemplate="<b>DDoS</b><br>Date: %{x}<br>Count: %{y}<extra></extra>"
+)
+fig.update_layout(
+    xaxis_title="Date",
+    yaxis_title="Number of DDoS Attacks",
+    showlegend=False
+)
+fig.show()
 
+print("\n" + "=" * 60)
+print("ANALYSIS COMPLETE")
+print("=" * 60)
 
+# %%