🤖 Machine Learning & Data Analytics Basic Portfolio Projects

Welcome to the Machine Learning & Data Analytics Projects repository! This collection showcases 7 end-to-end projects spanning regression analysis, binary classification, tabular feature engineering, decision trees, exploratory data analysis (EDA), time-series analytics, and natural language processing (NLP).

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🚀 Projects Overview

#	Project Name	Task / Domain	Primary Dataset	Tech & Algorithms	Key Metric
01	Student Marks Prediction	Regression / Education	UCI Student Performance	Linear Regression	$R^2$ = 0.78 (MAE: 1.34)
02	California House Price Prediction	Regression / Real Estate	California Housing	Linear Reg vs. Random Forest	$R^2$ = 0.805 (MAE: $32.7k)
03	Telco Customer Churn Prediction	Imbalanced Classification	IBM Telco Churn	Logistic Reg (Balanced) vs. Random Forest	Recall = 79.7% (ROC-AUC: 0.835)
04	Titanic Survival Prediction	Classification / Historical	Kaggle / OpenML Titanic	Logistic Reg vs. Decision Tree	Accuracy = 80.3% (F1: 0.720)
05	Movie Review Sentiment Analysis	NLP Sentiment Analysis	Stanford IMDb Reviews	TF-IDF + Logistic Regression	Accuracy = 76.0% (ROC-AUC: 0.845)
06	Retail Sales Data Analysis	EDA & Business Dashboards	Retail Sales Transactions	Pandas, NumPy, Matplotlib	$749.1k Total Revenue
07	Weather Time-Series Analytics	Time-Series & Climate EDA	Daily Weather Observations	Pandas, NumPy, Matplotlib	365-Day Rolling Temp & Rain

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📁 Repository Directory Structure

ML-Basic-Project/
│
├── 01-Student-Marks-Prediction/
│   ├── data/student-mat.csv
│   ├── student_marks_prediction.py
│   ├── actual_vs_predicted.png
│   ├── requirements.txt
│   └── README.md
│
├── 02-House-Price-Prediction/
│   ├── data/california_housing.csv
│   ├── house_price_prediction.py
│   ├── actual_vs_predicted.png
│   ├── requirements.txt
│   └── README.md
│
├── 03-Customer-Churn-Prediction/
│   ├── data/Telco-Customer-Churn.csv
│   ├── customer_churn_prediction.py
│   ├── confusion_matrix.png
│   ├── requirements.txt
│   └── README.md
│
├── 04-Titanic-Survival-Prediction/
│   ├── data/titanic.csv
│   ├── titanic_survival.py
│   ├── confusion_matrix.png
│   ├── requirements.txt
│   └── README.md
│
├── 05-Movie-Sentiment-Analysis/
│   ├── data/imdb_reviews.csv
│   ├── movie_sentiment_analysis.py
│   ├── prepare_dataset.py
│   ├── confusion_matrix.png
│   ├── requirements.txt
│   └── README.md
│
├── 06-Sales-Data-Analysis/
│   ├── data/sales_data.csv
│   ├── sales_analysis.py
│   ├── sales_dashboard.png
│   ├── requirements.txt
│   └── README.md
│
├── 07-Weather-Data-Analytics/
│   ├── data/weather_data.csv
│   ├── weather_analytics.py
│   ├── weather_dashboard.png
│   ├── requirements.txt
│   └── README.md
│
├── .gitignore
└── README.md

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🛠️ Environment Setup & Quick Start

1. Clone the Repository

git clone https://github.com/SparshM8/ML-Basic-Project.git
cd ML-Basic-Project

2. Install Dependencies

Install core dependencies for all 7 projects:

pip install pandas scikit-learn matplotlib numpy

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📊 Summary of Project Learnings

1. Exploratory Data Analysis & Cleaning: Handled missing values, encoded categorical variables, transformed raw text into TF-IDF numerical vectors.
2. Time-Series & Business Analytics: Applied rolling averages, grouped time aggregations, and rendered multi-panel Matplotlib dashboards.
3. Model Selection & Benchmarking: Evaluated parametric baseline models (Linear/Logistic Regression) against non-parametric tree & ensemble models (Decision Trees, Random Forests).
4. Evaluation Metrics: Selected domain-tailored metrics such as MAE/RMSE for house price regression, $R^2$ for marks prediction, Recall for churn targeting, and F1-score/ROC-AUC for class-imbalanced datasets.

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📝 License

This project is licensed under the MIT License — see the LICENSE file for details.