Welcome to my GitHub repository! This repository showcases the projects I completed during my internship at eCodeCamp (Pvt.) Ltd. Each project represents a significant milestone in my journey through data science, machine learning, and web development.
- Project 1: Customer Churn Analysis
- Project 2: Titanic Survival Prediction
- Project 3: Image Classification with CNNs
In this project, I conducted an Exploratory Data Analysis (EDA) on a customer churn dataset. The primary objective was to identify patterns and trends that could help businesses understand the factors leading to customer churn and improve their retention strategies.
- Data Exploration: Extracted meaningful insights regarding customer behavior.
- Data Visualization: Created comprehensive visualizations like histograms and scatter plots to effectively communicate findings.
- Business Insights: Provided actionable insights that can influence customer retention strategies.
- Python
- Pandas
- Matplotlib & Seaborn
You can find the project files in the Customer Churn Analysis directory.
This project involved developing a Titanic Survival Prediction Web Application that predicts the likelihood of a passenger’s survival on the Titanic based on various input features.
- Interactive User Interface: Users can interact with the app via an age slider, passenger class selection, gender dropdown, and port of embarkation dropdown.
- Machine Learning Model: Implemented a
RandomForestClassifierthat achieved 85% accuracy and an 89% F1 score. - Web Deployment: Deployed using Flask, offering real-time predictions and smooth user interactions.
- Python
- Scikit-learn
- Flask
- HTML/CSS
You can find the project files in the Titanic Survival Prediction directory.
In my final internship project, I developed an interactive Image Classification System using Convolutional Neural Networks (CNNs). The model is capable of classifying images with high accuracy and is accessible through a user-friendly web interface.
- Advanced CNN Architecture: Designed a robust CNN model with multiple layers, batch normalization, dropout, and advanced regularization techniques.
- High Accuracy: Achieved 92% accuracy on test images and 87% on unseen images.
- User-Friendly Web Interface: Users can upload images and receive instant classification results through an intuitive and visually appealing interface.
- Python
- TensorFlow/Keras
- Flask
- HTML/CSS
You can find the project files in the Image Classification with CNNs directory.
Special thanks to eCodeCamp (Pvt.) Ltd for the opportunity to work on these impactful projects.
Feel free to explore the projects and share your thoughts. Your feedback is greatly appreciated! If you’d like to connect or discuss the projects further, please reach out via LinkedIn.