🚀 Machine Learning Project Series

This repository contains a collection of Machine Learning, Deep Learning, and Computer Vision projects developed by me as part of continuous learning and hands-on practice.

Each project in this series focuses on real-world datasets, practical implementation, and end-to-end workflows, including data preprocessing, modeling, evaluation, and visualization.

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📂 Projects Included

🌧️ Rainfall Prediction using Machine Learning

• Built a machine learning pipeline to predict rainfall using historical weather data
• Performed extensive Exploratory Data Analysis (EDA) including:
  • Boxplots for outlier detection
  • Feature distribution analysis
  • Correlation heatmaps
• Applied supervised learning techniques for binary classification (Rain / No Rain)

📁 Folder: Rainfall_Prediction_Using_Machine_Learning/

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🌾 Wheat Head Detection using Faster R-CNN

• Implemented object detection using Faster R-CNN with ResNet50 backbone
• Trained the model on agricultural field images
• Visualized predicted bounding boxes for detected wheat heads
• Gained hands-on experience with PyTorch and computer vision pipelines

📁 Folder: Wheat_detection_Faster_RCNN/

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🧠 Image Classification using InceptionV3

• Applied transfer learning with InceptionV3
• Trained a custom classifier on the CIFAR-10 dataset
• Performed real-world image inference using ImageNet pretrained weights
• Focused on CNN feature extraction and model fine-tuning

📁 Folder: Object_Detection_Using_InceptionV3/

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🎨 Generate Anime Faces using DCGAN

• Built a Deep Convolutional Generative Adversarial Network (DCGAN)
• Trained the model to generate synthetic anime face images
• Implemented generator–discriminator training from scratch
• Visualized generated images across training epochs

📁 Folder: Generate_Anime_Faces_DCGAN/

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🎯 Purpose of This Repository

• To document my learning journey in Machine Learning and Deep Learning
• To build strong hands-on projects beyond tutorials
• To create a portfolio-ready collection of ML/DL work
• To improve understanding of real-world data and modeling challenges

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🛠 Technologies Used

• Python
• NumPy, Pandas
• Scikit-learn
• TensorFlow / Keras
• PyTorch
• Matplotlib, Seaborn
• OpenCV

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👨‍💻 Author

Rushikesh Raghatate
Computer Science | Machine Learning | Deep Learning | Computer Vision

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⭐ This repository will be continuously updated with more advanced projects.