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Added Linear Regression and Support Vector Machine Modules #3

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358 changes: 358 additions & 0 deletions Linear Regression.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import linear_model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
" area price\n",
"0 2600 550000\n",
"1 3000 565000\n",
"2 3200 610000\n",
"3 3600 680000\n",
"4 4000 725000"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('homeprices.csv')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x1a171eab70>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.xlabel('Area (Sq.ft)')\n",
"plt.ylabel('Prices (USD)')\n",
"plt.scatter(df.area, df.price, color='red', marker='+')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
" normalize=False)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reg = linear_model.LinearRegression()\n",
"reg.fit(df[['area']], df.price)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"628715\n"
]
}
],
"source": [
"result = reg.predict([[3300]])\n",
"print(round(int(result)))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"628715\n"
]
}
],
"source": [
"m = reg.coef_\n",
"b = reg.intercept_\n",
"x = 3300\n",
"y = m*x+b\n",
"print(round(int(y)))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"d = pd.read_csv('areas.csv')\n",
"p = reg.predict(d)\n",
"prices = []\n",
"for i in p:\n",
" prices.append(round(int(i)))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area</th>\n",
" <th>prices</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1000</td>\n",
" <td>316404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1500</td>\n",
" <td>384297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2300</td>\n",
" <td>492928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3540</td>\n",
" <td>661304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4120</td>\n",
" <td>740061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4560</td>\n",
" <td>799808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5490</td>\n",
" <td>926090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3460</td>\n",
" <td>650441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4750</td>\n",
" <td>825607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2300</td>\n",
" <td>492928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>9000</td>\n",
" <td>1402705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>8600</td>\n",
" <td>1348390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>7100</td>\n",
" <td>1144708</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" area prices\n",
"0 1000 316404\n",
"1 1500 384297\n",
"2 2300 492928\n",
"3 3540 661304\n",
"4 4120 740061\n",
"5 4560 799808\n",
"6 5490 926090\n",
"7 3460 650441\n",
"8 4750 825607\n",
"9 2300 492928\n",
"10 9000 1402705\n",
"11 8600 1348390\n",
"12 7100 1144708"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d['prices'] = prices\n",
"d"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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