Data Science & Machine Learning Projects

This repository contains a collection of Python Projects for Data Science, Analysis, Preprocessing, and Machine Learning. It covers essential Python libraries, Data Manipulation techniques, Scaling, Regression, Classification, and Practical Applications on Real Datasets.

---

Projects Structure:

1. Basic Libraries
   Fundamental Python libraries:

   • NumPy – Numerical Computing
   • Matplotlib – Basic Plotting
   • Seaborn – Advanced Visualizations

2. Pandas Project

   • Analysis on car_data.csv using Pandas.
   • Demonstrates 30 commonly used Pandas functions for Data Manipulation.

3. Scaling

   • Explains Standardisation and Normalisation.
   • Mathematical walkthrough and Python implementation.

4. Regression Project

   • Applies Linear and Logistic Regression on lifestyle.csv.
   • Focus on model training, prediction, and evaluation metrics.

5. Iris Analysis

   • Quick analysis of the built-in Iris Dataset.
   • Logistic Regression (Classification).

6. Decision Tree & Random Forest Project

   • Applied Decision Tree and Random Forest Classifiers on camera_dataset.csv.
   • Evaluation using Accuracy and Feature Importance.

7. KNN Project

   • Explains basics of K-Nearest Neighbors (KNN) for Regression and Classification.
   • Applied on _bills.csv Dataset to classify fake or real bills.

8. E-Challan Cameras Project

   • In-depth Analysis on cameras.csv to identify the best cameras for a Traffic E-Challan System.
   • Preprocessing & Feature Engineering
   • Rule-based Suitability Score using MinMax Scaling

   ML Applications:

   • Regression: Predict the rule-based score using LinearRegression and RandomForestRegressor.

   • Classification: Convert score to classes (High, Medium, Low) and predict using LogisticRegression and RandomForestClassifier.

   Results & Insights:

   • Regression achieved R² > 0.98 and Classification achieved 97% Accuracy.
   • Key features influencing suitability: Low and Effective Resolution.
   • Combines rule-based and ML methods for transparent, data-driven decisions.

---

Requirements

• Python 3.10+
• Libraries: numpy, pandas, matplotlib, seaborn, scikit-learn

You can install dependencies via:

pip install -r requirements.txt