Movie Revenue Prediction System predicts the revenue of a movie with 14 parameters: name, rating, genre, year, released, score, votes, director, writer, star, country, budget, company and runtime using gradient boosting______________________________ Training Accuracy: 91.58%____________ Testing Accuracy: 82.42%.
Implementation of ML algorithms for FlipKart Product Category Classification based on the product's description and other features.
This is a portotype of GitHub recommender system.
A credit card fraud detection model using Random Forest on an imbalanced dataset of 284,807 transactions, achieving an F1-score of 0.69 and precision of 84%, with complete EDA and evaluation metrics.
Some dimensionality reduction and clustering examples using sklearn library
DE and DA samples
Real time face detection and recognition integrating with CCTV
An interactive web application built with Streamlit to assess the credit risk of bank customers using supervised machine learning models. The system allows users to make individual default probability predictions, visualize model performance metrics, and analyze the underlying dataset, all from a user-friendly interface for business users.
Analyzed a 23-feature dataset, targeting 'RainTomorrow' for weather insights. Conducted thorough data gathering, preprocessing, and feature selection. Evaluated diverse models (Logistic Regression, Random Forest, Decision Trees, K-means, K-nearest neighbors, Hierarchical clustering) and employed technical metrics for in-depth performance analysis.
This is a regression problem to predict california housing prices.
Kaggle fun house
A Random Forest Approach to Estimate Rainfall
Spam Classifier using Naive Bayesian Method
📚 Cluster Analysis of Data Literacy Research — Revealing patterns & gaps through hierarchical clustering
Iris Classification using KNN as weekly task of Decode Labs Internship
Projet réalisé pour INSPIRE d'Article 1, mise en place d'algorithme de modération.
Machine Learning Practices
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