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🔍 Machine Learning Lab Repository

Welcome to the Machine Learning Lab repository! This repository includes seven comprehensive lab assignments and a mini project, each aimed at developing practical skills in data preprocessing, exploratory data analysis (EDA), model development, evaluation, and optimization using real-world datasets.

📁 Lab 1: Titanic Survival Prediction

Objective:
Prepare the Titanic dataset for machine learning and perform predictive analysis.

Tasks:

  • Perform basic EDA using head(), tail(), describe(), shape
  • Handle missing values and duplicates
  • Identify and treat outliers
  • Apply data encoding techniques
  • Conduct Univariate, Bivariate & Multivariate analysis
  • Perform feature scaling
  • Split dataset (80:20)

📁 Lab 2: Used Car Price Prediction

Objective:
Build a regression model to estimate the selling price of used cars.

Tasks:

  • Perform EDA
  • Apply Linear Regression & Multiple Linear Regression
  • Evaluate model using MAE, MSE, RMSE, R² Score
  • Apply hyperparameter tuning

📁 Lab 3: Customer Purchase Prediction

Objective:
Predict if a customer will purchase a product based on behavior and demographics.

Tasks:

  • Perform EDA
  • Apply Logistic Regression, Decision Tree, and KNN
  • Evaluate using Accuracy, Precision, Recall, and F1-Score
  • Apply hyperparameter tuning

📁 Lab 4: Dimensionality Reduction with PCA

Objective:
Classify hand-written digits using logistic regression after reducing dimensions.

Tasks:

  • Apply Principal Component Analysis (PCA)
  • Train model using Logistic Regression
  • Evaluate classification performance

📁 Lab 5: Loan Default Prediction Using Ensemble Methods

Objective:
Predict loan default risk using ensemble techniques.

Tasks:

  • Perform EDA
  • Apply Random Forest and AdaBoost classifiers
  • Evaluate using Accuracy, Precision, Recall, and F1-Score
  • Develop an ANN model for comparison
  • Apply hyperparameter tuning

📁 Lab 6: Heart Attack Risk Prediction

Objective:
Predict whether a person is at risk of a heart attack.

Tasks:

  • Perform EDA
  • Apply Naive Bayes and Support Vector Machine (SVM)
  • Evaluate using Accuracy, Precision, Recall, and F1-Score
  • Apply hyperparameter tuning

📁 Lab 7: Customer Segmentation with K-Means

Objective:
Help a shopkeeper segment customers for targeted marketing.

Tasks:

  • Perform EDA
  • Apply K-Means Clustering algorithm
  • Visualize clusters and provide business insights

📁 Mini Project: Classification Model Comparison

Objective:
Build and compare multiple classification models on a chosen dataset.

Tasks:

  • Choose dataset of your interest
  • Perform detailed EDA
  • Apply the following algorithms:
    • Logistic Regression (LR)
    • Naive Bayes (NB)
    • Support Vector Machine (SVM)
    • K-Nearest Neighbors (KNN)
    • Decision Tree (DT)
    • Random Forest (RF)
    • AdaBoost
  • Evaluate using classification metrics
  • Compare all models and visualize results

🛠 Technologies Used

  • Python
  • Pandas, NumPy, Matplotlib, Seaborn
  • Scikit-learn
  • TensorFlow / Keras (for ANN)
  • Jupyter Notebooks

📌 How to Use

  1. Clone this repository: 
    git clone https://github.com/yourusername/machine-learning-labs.git