A curated collection of end-to-end Data Science projects, demonstrating hands-on skills in data analysis, machine learning, and AI.
| Category | Skills & Tools |
|---|---|
| Programming | Python, NumPy, Pandas, SQL |
| Visualization | Matplotlib, Seaborn, Plotly, Tableau |
| Machine Learning | Decision Trees, Random Forest, SVM, KNN, Logistic Regression |
| Advanced ML / AI | XGBoost, LightGBM, Neural Networks, MLP, Deep Learning |
| Data Science | EDA, Feature Engineering, PCA, Hypothesis Testing, Statistics |
| NLP & Text Mining | Naive Bayes, Text Processing, Sentiment Analysis |
| Time Series & Forecasting | ARIMA, Prophet |
| Recommender Systems | Collaborative Filtering, Content-Based Filtering |
| Tools | Git, Jupyter Notebook, VS Code, Google Colab |
| # | Project | Focus / Techniques |
|---|---|---|
| 1 | Association Rules | Market Basket Analysis, Apriori Algorithm |
| 2 | Basics of Python 1 & 2 | Python fundamentals & scripting |
| 3 | Basic Statistics | Descriptive & Inferential Statistics |
| 4 | Chi-Square Test | Categorical Data, Hypothesis Testing |
| 5 | Clustering | K-Means, Hierarchical Clustering |
| 6 | Decision Tree | Classification & Regression |
| 7 | EDA 1 & 2 | Data Visualization, Insights Extraction |
| 8 | Hypothesis Testing | t-tests, ANOVA, p-values |
| 9 | KNN | Classification, Distance Metrics |
| 10 | LightGBM & XGBoost | Gradient Boosting, Hyperparameter Tuning |
| 11 | Logistic Regression | Binary & Multiclass Classification |
| 12 | Multilayer Perceptron (MLP) | Neural Networks for tabular data |
| 13 | Naive Bayes & Text Mining | NLP, Sentiment Analysis |
| 14 | Neural Networks | Deep Learning, TensorFlow / PyTorch |
| 15 | Random Forest | Ensemble Learning, Feature Importance |
| 16 | PCA | Dimensionality Reduction |
| 17 | Recommendation System | Collaborative & Content-Based Filtering |
| 18 | SVM | Classification, Kernel Methods |
| 19 | Time Series Analysis | Forecasting Models, ARIMA, Prophet |
- Data Collection & Cleaning – transform raw data into analysis-ready format.
- EDA & Visualization – discover patterns, trends, and anomalies.
- Feature Engineering & Selection – extract meaningful features.
- Modeling – build, tune, and evaluate classical & advanced ML models.
- Insights & Reporting – actionable business insights from data.
1.FAANG-Level Skills: Demonstrates data science, ML, AI, and problem-solving skills.
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End-to-End Projects: From raw data to deployable insights.
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Clean & Reproducible Code: Well-commented, structured, and ready for production.
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Real-World Applications: Projects emulate business and analytical challenges.