This repository contains six machine learning projects, each demonstrating different ML algorithms with various datasets. Each project includes data preprocessing, model building, evaluation, and insights.
- Dataset:
taxi_trip_pricing.csv - Objective: Predict taxi trip prices based on multiple factors such as distance, time, and fare conditions.
- Algorithm: Multiple Linear Regression
- Key Steps: Data cleaning, feature selection, model training, and evaluation.
- Dataset:
Ice_cream_selling_data.csv - Objective: Predict ice cream sales based on temperature and other seasonal factors.
- Algorithm: Polynomial Regression
- Key Steps: Feature engineering, polynomial transformation, model training, and evaluation.
- Dataset:
Titanic-Dataset.csv - Objective: Predict whether a passenger survived based on available demographic and travel details.
- Algorithm: K-Nearest Neighbors (KNN) Classification
- Key Steps: Data preprocessing, feature scaling, model training, and evaluation.
- Dataset:
loan_approval_dataset.csv - Objective: Predict loan approval based on customer demographics and financial history.
- Algorithm: Random Forest Classification
- Key Steps: Handling missing values, feature encoding, model training, and hyperparameter tuning.
- Dataset:
penguins.csv - Objective: Group different species of penguins based on their physical features.
- Algorithm: K-Means Clustering
- Key Steps: Data scaling, clustering analysis, evaluation using silhouette scores.
- Dataset:
Market_Basket_Optimisation.csv - Objective: Identify frequent item sets and generate association rules for market basket optimization.
- Algorithm: Apriori Algorithm
- Key Steps: Data transformation, rule generation, interpretation of results.