Model Selection Techniques For Regression Problems.

In this project, we used various methods such simple Linear Regression, Ridge Regression, LASSO Regression, Elastic Net, Polynomial Regression and Robust Regression to predict housing price. We compared the results in order choose the best model.

Diabetes Prediction Using Machine Learning Algorithms.

In this project, we utilized a variety of Machine Learning Algorithms, including Random Forest, Decision Tree, SVM, and k-Nearest Neighbor and XBOOST, to predict diabetes in patients. We specifically utilized the Pima Indians Diabetes Database for this analysis.

Logistic Regression For Rainy Day Prediction.

In this project, we employed logistic regression algorithms to forecast rain in Australia. Our analysis focused on the Rains in Australia Dataset for accurate predictions.

Neural Networks For Time series Forecasting.

In this project, we utilized neural networks, specifically LSTM models, to tackle a variety of time series problems, including multivariate forecasting for Multiple Parallel Series.

Neural Network For Predicting Stock Prices.

In this project, we created a neural network to predict the price of a stock. We used TSLA.csv data set. We implemented the neural network with the following details: - Sequential model with 5 layers. - 2 LSTM layers, and after each use a Dropout layer with a rate of 0.2.

Deep Convolutional Neural Networks For Images Classification

In this project, we utilized Deep Convolutional Neural Networks to accurately classify images of dogs and cats. Following training, the model effectively distinguishes between the two animals in a given image.

Wine Quality Classification.

In this project, I utilized a dataset of wine quality to categorize wines based on their unique compositions. To achieve this classification, clustering algorithms are employed. Random forest is utilized to determine the importance of each variable in order to classify the wines based on feature