End to end machine learning pipeline for house price prediction using various models, the pipeline includes exploratory data analysis (EDA), data preprocessing, models training & evaluating

A Java console application that implemetns k-fold-cross-validation system to check the accuracy of predicted ratings compared to the actual ratings and RMSE to calculate the ideal k for our dataset.

This repository utilizes time series analysis to predict natural gas prices, aiding informed decisions in the energy market. Through meticulous data preprocessing, visualization, and ARIMA modeling, it provides accurate forecasts. With regression and interpolation techniques, it offers deeper insights for stakeholders, enabling proactive strategies

script for simulation of a Student gradebook & statistical calculation of class averages.

BenchMetrics Prob: Benchmarking of probabilistic error performance evaluation instruments for binary-classification problems

Covid-19 prediction (For Nepal) with different MODELS (Sigmoidal, Linear Regressor, Random Forest Regressor) and comparisons

Techniques for data mining.

This Kaggle competition challenges participants to use a modified version of the POWER NASA Temperature Dataset to build forecasting models.

In linear regression, regularization is a process of making the model more regular or simpler by shrinking the model coefficient to be closer to zero or absolute, ultimately to address over fitting.

The "Advertising Impact Analysis" project aims to analyze the relationship between advertising expenditure across different channels (such as TV, radio, online) and its impact on sales or revenue.

Population Prediction forecasts the Haggis population on a mountain. Ecologists have recorded the population over five years and have satellite estimates. The goal is to predict the true population 12 months ahead using machine learning and time series analysis techniques. This project is for the COM6509 - Machine Learning and Adaptive Intelligence

Intrusion Detection System for MQTT Enabled IoT.

Building a logistic regression from scratch without using any external machine learning library. It is done to demonstrate my understanding of the intuition of the logistic regression model

Hybrid recommender system - Collaborative filtering + Content based filtering (same as used by Netflix).

Some codes that I prepared for fundamental research interests.

Using machine learning, get the features and labels from the csv and perform cross fold validation and linear regression.

Developed a sophisticated house price prediction model incorporating data pre-processing, feature engineering, and Linear Regression, validated by Root Mean Squared Error (RMSE) evaluation.

Machine Learning Regression Model to Predict Energy Efficiency of Buildings