https://github.com/sankhya007/winter-internship-projects-2025

https://github.com/sankhya007/winter-internship-projects-2025

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• Host: GitHub
• URL: https://github.com/sankhya007/winter-internship-projects-2025
• Owner: sankhya007
• License: mit
• Created: 2025-11-18T18:47:25.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-11-18T20:39:06.000Z (7 months ago)
• Last Synced: 2025-11-18T21:05:56.247Z (7 months ago)
• Language: Python
• Size: 3.88 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Winter Internship Projects 2025

## Project 1: Housing Data Analysis with Python

### 📊 Project Description

Complete data analysis of California housing dataset using Pandas and Matplotlib as per internship requirements.

### ✅ Tasks Completed

- Loaded CSV file using Pandas library

- Performed basic data analysis and calculated averages

- Created visualizations: Bar charts, Scatter plots, Heatmaps

- Provided detailed insights and observations

### 🛠 Technologies Used

- Python

- Pandas

- NumPy

- Matplotlib

- Scikit-learn

### 📁 Files

- `complete_analysis.py` - Basic data analysis code

- `housing.csv` - Dataset

- `requirements.txt` - Dependencies

- `README.md` - Project documentation

### 🚀 How to Run

1. Install dependencies: `pip install -r requirements.txt`

2. Run analysis: `python complete_analysis.py`

---

## Project 2: House Price Prediction using Linear Regression

### 🏠 Advanced House Price Prediction System

A comprehensive machine learning project that predicts California

housing prices using multiple regression algorithms with advanced

feature engineering and visualization.

### 📊 Project Overview

This project implements an end-to-end machine learning pipeline for

predicting median house values in California districts. The system

includes advanced data preprocessing, feature engineering, multiple

model comparison, and comprehensive visualization.

### 🚀 Features

-   Advanced Data Preprocessing: Automated missing value imputation and

    data validation

-   Feature Engineering: Created interaction features, location-based

    metrics, and categorical encoding

-   Multiple Algorithms: Linear Regression, Ridge, Lasso, and Random

    Forest

-   Model Comparison: Comprehensive evaluation with cross-validation

-   Advanced Visualizations: Interactive charts and business

    intelligence reports

-   Feature Importance: Detailed analysis of price drivers

-   Confidence Intervals: Prediction ranges with statistical confidence

### 🛠️ Installation

1.  Clone the repository:

    ```bash

    git clone https://github.com/yourusername/house-price-prediction.git

    cd house-price-prediction

    Install dependencies:

    bash

    pip install -r requirements.txt

    Ensure you have the housing.csv dataset in the project root.

### 🎯 Usage

Run the main script:

    python linear_regression_model.py

The script will:

-   Load and validate the dataset

-   Perform advanced preprocessing and feature engineering

-   Train multiple machine learning models

-   Generate comprehensive visualizations

-   Provide business insights and predictions

### 📈 Model Performance

The system compares multiple algorithms:

-   Linear Regression: Baseline performance

-   Ridge Regression: Regularized linear model

-   Lasso Regression: Feature selection capabilities

-   Random Forest: Ensemble tree-based approach

### 🔍 Key Insights

-   Identifies top price-driving features

-   Provides confidence intervals for predictions

-   Generates strategic business recommendations

-   Visualizes model performance and data relationships

### 📊 Sample Predictions

The system includes pre-configured property profiles:

-   Luxury Coastal Villa: High-end properties

-   Family Suburban Home: Middle-income housing

-   Investment Opportunity: Value properties

### 🧪 Technical Details

Dataset: California Housing Prices (20,640 samples, 10 features)

Preprocessing: StandardScaler, VarianceThreshold, SimpleImputer

Validation: 80-20 train-test split, 3-fold cross-validation

Metrics: R² Score, MAE, RMSE, Cross-validation scores

## Project 3: Matrix Operations Tool

A comprehensive Python application for performing various matrix

operations using NumPy. Features an interactive command-line interface

for easy matrix manipulation and analysis.

### Features

-   Matrix Input: Interactive matrix input with validation

-   Basic Operations: Addition, Subtraction, Multiplication

-   Advanced Operations: Transpose, Determinant, Inverse

-   Matrix Management: Store, view, and delete multiple matrices

-   Error Handling: Comprehensive input validation and error messages

-   Demo Mode: Preloaded matrices for quick testing

### Operations Supported

1.  Matrix Addition

2.  Matrix Subtraction

3.  Matrix Multiplication

4.  Matrix Transpose

5.  Matrix Determinant

6.  Matrix Inverse

7.  Input New Matrix

8.  View All Matrices

9.  Delete Matrix

### Installation

1.  Ensure you have Python installed on your system.

2.  Install the required dependency:

    pip install numpy

3.  Run the application:

    python matrix_operation.py

### Usage

-   Start the application and choose whether to load demo matrices.

-   Use the menu to perform operations:

    -   First, input matrices using option 7.

    -   Then perform operations like addition, multiplication, etc.

-   View results in formatted output.

-   Results are automatically stored for further operations.

### Project Structure

matrix_operation_tools/ - matrix_operation.py - housing.csv - README.md

### Technical Details

-   Built With: Python, NumPy

-   Architecture: Object-oriented design

-   Error Handling: Comprehensive validation

-   UI: Clean command-line interface

### 🤝 Contributing

-   Fork the project

-   Create your feature branch (git checkout -b feature/AmazingFeature)

-   Commit your changes (git commit -m ‘Add some AmazingFeature’)

-   Push to the branch (git push origin feature/AmazingFeature)

-   Open a Pull Request

### 📄 License

This project is licensed under the MIT License - see the LICENSE file

for details.

### 👥 Authors

- **Sankhyapriyo Dey** - *Initial work* - [sankhya007](https://github.com/sankhya007)

### 🙏 Acknowledgments

-   Dataset sourced from California Housing Prices

-   Scikit-learn for machine learning algorithms

-   Matplotlib and Seaborn for visualizations

-   png image visualization