Fastag Fraud Detection Classification System

In this project the task is to predict charges or cost of the person on the basis of his/her lifestyle, smoking habit, number of children's and person's home location.

Comparision of classifiers titanic dataset

Developed student performance predicting model, showing strong understanding of predictive modeling techniques.

Faces recognition project using Support Vector Machines (SVM) and Principal Component Analysis (PCA). It utilizes the Labeled Faces in the Wild (LFW) dataset, employs dimensionality reduction with PCA, and fine‑tunes SVM hyperparameters using RandomizedSearchCV.

Parkinson disease detection using KNN Classifier and Random Forest Classifier.

The ability to predict prices and features affecting the appraisal of property can be a powerful tool in such a cash intensive market for a lessor. Additionally, a predictor that forecasts the number of reviews a specific listing will get may be helpful in examining elements that affect a property's popularity.

Exploring the intersection of supervised machine learning algorithms and weather data to drive ClimateWins forward. (CF student project)

Different techniques to tune the hyperparameter of machine learning models.

Telecom Churn prediction with multiple machine learning models

Bank Customer Behaviour Prediction

Model to predict bank customer churn

This is a friend recommendation systems which are used on social media platforms (e.g. Facebook, Instagram, Twitter) to suggest friends/new connections based on common interests, workplace, common friends etc. using Graph Mining techniques. Here, we are given a social graph, i.e. a graph structure where nodes are individuals on social media plat…

Diabetes Prediction with Tree based models (Random Forest and XGBoost). Grid Search CV and Randomized Search CV used to optimize parameters

A Machine Learning Regression Model has been used to predict the prices for houses in Boston.

Comparative Analysis of Decision Tree Algorithms in Number Classification: Bagging vs. Random Forest vs. Gradient Boosting Decision Tree Classifiers

An active competition on Zindi which involves estimating the crop yield for farms in India with a focus on Bayesian Optimization

A New, Interactive Approach to Learning Python

A comprehensive analysis and predictive modeling of the "Salary Data.csv" dataset to forecast salaries. Utilizes advanced machine learning techniques, including pipelines and transformers, for robust and accurate predictions.

Developed a churn prediction classification model using various techniques including: EDA, Decision trees, Naive Bayes, AdaBoost, MLP, Bagging, RF, KNN, logistic regression, SVM, Hyperparameter tuning using Grid Search CV and Randomized Search CV.