AI & Machine Learning Projects Collection

This repository contains multiple Machine Learning and Deep Learning projects built using Python, Streamlit, Scikit-learn, TensorFlow, Pandas, NumPy, and Matplotlib. The projects focus on real-world AI applications including classification, prediction, computer vision, and business analytics.

The repository includes complete source code, datasets, trained models, preprocessing pipelines, and interactive Streamlit dashboards.

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Projects Included

1. Bengaluru Housing Price Prediction

A Machine Learning web application that predicts house prices in Bengaluru based on property details such as area, number of bedrooms, and bathrooms.

Features

• Data preprocessing and cleaning
• Area conversion handling
• Linear Regression model
• Interactive Streamlit interface
• Real-time house price prediction
• Data visualization dashboard

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Screenshots

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Technologies Used

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Streamlit

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2. Handwritten Digit Recognition using CNN

A Deep Learning application that recognizes handwritten digits using a Convolutional Neural Network trained on the MNIST dataset.

Features

• CNN-based digit classification
• Interactive drawing canvas
• Image upload support
• Real-time prediction
• Prediction confidence visualization
• Cached model loading
• TensorFlow/Keras implementation

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Screenshots

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Technologies Used

• Python
• TensorFlow
• Keras
• NumPy
• PIL
• Streamlit
• streamlit-drawable-canvas

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3. Iris Flower Classification

A Machine Learning classification project that predicts the species of an Iris flower based on sepal and petal measurements.

Features

• Logistic Regression model
• Interactive sliders for input
• Prediction probability visualization
• Dataset viewer
• Model accuracy display
• Species information section

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Screenshots

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Technologies Used

• Python
• Scikit-learn
• Pandas
• NumPy
• Streamlit

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4. Telecom Customer Churn Prediction Dashboard

A professional Machine Learning dashboard that predicts whether a telecom customer is likely to churn based on customer behavior and service usage.

Features

• Data preprocessing and encoding
• Random Forest Classification model
• Customer churn prediction
• Probability score calculation
• KPI dashboard
• Churn distribution visualization
• Interactive sidebar controls
• Model accuracy evaluation

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Screenshots

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Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Streamlit

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Repository Structure

├── dataset/
│   ├── Bengaluru_House_Data.csv
│   ├── Telco-Customer-Churn.csv
│
├── Housing_Price_Prediction/
│   ├── app.py
│
├── Digit_Recognition_CNN/
│   ├── app.py
│   ├── mnist_model.h5
│
├── Iris_Classification/
│   ├── app.py
│
├── Customer_Churn_Prediction/
│   ├── app.py
│
├── requirements.txt
├── README.md

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Installation

Clone the repository:

git clone https://github.com/your-username/your-repository-name.git

Move into the project directory:

cd your-repository-name

Install dependencies:

pip install -r requirements.txt

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Running Streamlit Applications

Run any project using:

streamlit run app.py

Example:

cd Iris_Classification
streamlit run app.py

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Skills Demonstrated

This repository demonstrates practical implementation of:

• Machine Learning
• Deep Learning
• Data Preprocessing
• Model Training
• Model Evaluation
• Classification Algorithms
• Regression Algorithms
• CNN Architecture
• Data Visualization
• Streamlit Deployment
• Interactive Dashboard Development
• Feature Engineering
• Predictive Analytics

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Libraries Used

• Streamlit
• Pandas
• NumPy
• Scikit-learn
• TensorFlow
• Matplotlib
• PIL
• streamlit-drawable-canvas

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👨‍💻 Author

Omkar Koli