This repository contains my homework and practice tasks for the Machine Learning and IIOT4 training program. All assignments provided by my trainer are uploaded here as Jupyter Notebook files.
The purpose of this repository is to maintain a record of my learning progress and practical implementation of different Machine Learning concepts.
This repository includes various Machine Learning tasks such as:
- Data preprocessing and cleaning
- Exploratory Data Analysis (EDA)
- Feature engineering
- Machine Learning model implementation
- Model evaluation and testing
Each task is implemented using Python and executed in Jupyter Notebook.
The repository mainly contains Jupyter Notebook (.ipynb) files for different tasks assigned during the training.
Some of the Machine Learning tasks included in this repository are:
House Price Prediction
Accident Prediction
More tasks will be added as part of my ongoing training.
- Problem statement
- Data loading
- Data preprocessing
- Model building
- Model evaluation
- Results and observations
The following tools and libraries are used in the implementation:
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Scikit-learn
- Matplotlib / Seaborn (for data visualization)
The main objective of this repository is to:
- Practice Machine Learning concepts
- Implement algorithms using real datasets
- Track progress during the Machine Learning and IIOT4 training program
- Maintain a structured collection of assignments
Through these tasks, I aim to improve my understanding of:
- Data analysis techniques
- Machine Learning model development
- Data preprocessing methods
- Model evaluation strategies
Ayush Kumar
Machine Learning Trainee