Python Development Projects

Welcome to my repository of Python development projects focused on Data Science (DS), Artificial Intelligence (AI), and Machine Learning (ML). This repository includes a collection of projects showcasing various techniques and applications in these fields. Below is an overview of each project, including a brief description, technologies used, and how to get started.

Table of Contents

1. Customer Churn Prediction
2. Sentiment Analysis of Product Reviews
3. Sales Forecasting Using Time Series Analysis
4. Image Classification Using Convolutional Neural Networks
5. Getting Started
6. Contributing
7. License

Customer Churn Prediction

Description

Developed a machine learning model to predict customer churn using logistic regression and random forest algorithms.

Technologies

• Python
• Pandas
• Scikit-learn
• Flask
• SQL

Features

• Data preprocessing and cleaning
• Model training and evaluation
• API for model inference

Sentiment Analysis of Product Reviews

Description

Conducted sentiment analysis on product reviews using natural language processing techniques.

Technologies

• Python
• NLTK
• Keras
• Matplotlib

Features

• Text preprocessing (tokenization, stopwords removal)
• Sentiment analysis using VADER and LSTM neural network
• Visualization of results

Sales Forecasting Using Time Series Analysis

Description

Built a time series forecasting model to predict sales for the next quarter.

Technologies

• Python
• Pandas
• Statsmodels
• Plotly

Features

• Analysis of seasonal trends and patterns
• ARIMA model implementation
• Prediction visualization

Image Classification Using Convolutional Neural Networks

Description

Developed a deep learning model for image classification tasks using convolutional neural networks (CNNs).

Technologies

• Python
• TensorFlow
• Keras
• Image Processing

Features

• Training and testing of CNN models
• High accuracy image classification

Getting Started

Prerequisites

• Python 3.x
• Libraries: pandas, scikit-learn, flask, nltk, keras, matplotlib, statsmodels, plotly, tensorflow

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/python-development-projects.git
   cd python-development-projects

2. Install the required packages:

   pip install -r requirements.txt

Usage

Each project has its own directory. Refer to the README.md file within each project directory for detailed instructions on how to run the code.

Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to create an issue or submit a pull request.

Steps to Contribute

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

License

This repository is licensed under the MIT License. See the LICENSE file for more information.