📘 Machine Learning Journey — Priya Rathor

Welcome to my Machine Learning repository — a complete end-to-end journey from learning core concepts to building industry-grade ML and DL projects. This repo is structured for beginners to intermediate learners and includes:

• 📚 Daily topic-wise learning
• 🛠️ Real-world machine learning projects
• 📊 Data preprocessing and EDA notebooks
• 🤖 Algorithms from scratch
• 🔬 Deep learning experiments and research
• 🎯 Interview Q&A and ML readiness

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🗂️ Repository Structure

Machine-learning/
│
├── Datasets/                        # Sample datasets used across projects
├── Day1-Python/                    # Python Basics
├── Day2-EDA/                       # Exploratory Data Analysis
├── Day3-DataAnalysis/             # Data cleaning & feature exploration
├── Day4-oops/                     # OOPs concepts in Python
├── Day5-Numpy/                    # Numpy operations and tricks
├── Day6-Pandas/                   # DataFrame operations
├── Day7-matplotlib/               # Data Visualization - Matplotlib
├── Day8-seaborn/                  # Data Visualization - Seaborn
│
├── FeatureEncoding/              # Label Encoding, One-Hot Encoding
├── FeatureScaling/               # Standardization, Normalization
├── missingValues/                # Handling nulls, imputation
│
├── Bagging/                      # Ensemble technique
├── Decision Tree/                # Tree-based classifier
├── GradientDescent/             # Manual GD implementation
├── K NearestNeighbors/          # KNN classifier
├── LR-Algorithm/                # Linear Regression from scratch
├── LogisticRegression/          # Binary classifier implementation
├── Maths/                       # Stats, probability, algebra essentials
├── Naive Bayes/                 # Bayesian classifier
├── RandomForest/                # Ensemble technique
│
├── InterviewQ\&A/                # ML and DL interview prep
├── Projects/                    # Full-scale projects (see below)
├── UnitOne/Two/Three/          # Course/module-based structured practice
├── SeeDataset/                 # Dataset viewing/inspection notebooks
│
├── batch\_vs\_stochastic.ipynb   # Compare batch sizes in optimization
├── dropout\_classification.ipynb # Dropout in deep learning
├── early\_stopping.ipynb        # Overfitting prevention
├── feature\_scaling.ipynb       # Feature scaling visualization
├── vanishing\_gradient.ipynb    # Vanishing gradient exploration
│
├── Test.ipynb
├── main.py
└── .gitignore

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📆 Daily Learning Logs

This section includes hands-on practice by day:

Day	Topic	Highlights
Day 1	Python	Syntax, loops, functions, modules
Day 2	EDA	Visual + statistical exploration
Day 3	Data Analysis	Missing data, duplicates, types
Day 4	OOPs	Classes, inheritance, encapsulation
Day 5	NumPy	Arrays, slicing, reshaping
Day 6	Pandas	Merging, grouping, filtering
Day 7	Matplotlib	Visualization fundamentals
Day 8	Seaborn	Heatmaps, Pairplots, Distribution plots

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📊 ML Concepts

Covers preprocessing and core ML algorithm implementation from scratch:

🧹 Preprocessing

• missingValues/: Dropping, filling, interpolation
• FeatureEncoding/: LabelEncoder, OneHotEncoder
• FeatureScaling/: StandardScaler, MinMaxScaler, RobustScaler

🤖 Algorithms

• Linear Regression & Logistic Regression
• KNN, Naive Bayes, Decision Tree, Random Forest
• Gradient Descent (Manual + library-based)
• Bagging (Ensemble methods)

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📁 Projects

Each project is complete with data loading, preprocessing, model training, evaluation, and saving.

Project	Description
📊 BangaloreHousePricePrediction	Regression model to estimate house prices in Bangalore
📚 BookRecommenderSystem	Recommender using cosine similarity and collaborative filtering
🚗 CarPricePredictor	ML regression model for second-hand car pricing
🛍️ E-CommerceProductsRecommendationSystem	Personalized product recommendations
✉️ EmailSpamFilter	NLP-based spam classifier
🪙 GoldPricePredictor	Time-series forecasting of gold prices
💻 LaptopPricePredictor	Price prediction based on specs
🎥 MovieRecommenderSystem	Hybrid model (Content + Collaborative Filtering)
📱 sms-spam-detection	SMS spam classifier using Naive Bayes

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🔬 Deep Learning Experiments

Notebook	What it Shows
batch_vs_stochastic.ipynb	Mini-batch vs full-batch training
dropout_classification.ipynb	Dropout effect on neural networks
early_stopping.ipynb	Prevent overfitting in training
feature_scaling.ipynb	Importance of normalization
vanishing_gradient.ipynb	Effect on deep neural networks

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🎤 Interview Q&A

• Contains a curated list of most asked interview questions and answers in ML, DL, and Python.
• Covers:
  • Supervised vs Unsupervised
  • Bias-Variance Tradeoff
  • Feature Selection Techniques
  • Regularization, Overfitting
  • Evaluation Metrics (Accuracy, Precision, Recall, F1)
  • Cost Function, Gradient Descent

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💻 Setup Instructions

1. Clone the repository:

git clone https://github.com/Priya-Rathor/Machine-learning.git
cd Machine-learning

2. Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Launch Jupyter Notebook:

jupyter notebook

Or use directly in Google Colab if notebooks are available there.

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🛠 Tools & Libraries

• Languages: Python
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
• Notebook: Jupyter, Google Colab
• ML: Linear Models, Trees, Ensemble, Naive Bayes, KNN
• DL Concepts: Dropout, Early Stopping, Batch Training

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✨ Highlights

✅ All models and projects are reproducible ✅ Clear file organization for learning and projects ✅ Preprocessing and math from scratch ✅ Visualizations and insights on real data ✅ Ideal for ML interview preparation

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🙋‍♀️ About Me

I’m Priya Rathor, passionate about building intelligent systems using AI/ML. This repository is my personal journey as I explored Python, ML algorithms, data preprocessing, and real-world use cases through consistent practice.

🔗 GitHub: Priya-Rathor

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⭐️ Show Support

If this project helped you, consider:

• 🌟 Starring the repo
• 🍴 Forking to learn your way
• 🤝 Connecting on GitHub

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