artificial-neural-network

In this project, I used artificial neural networks to perform regression on energy dataset and classification tasks on Churn Modelling datasets.

association-rules

I used machine learning to apply association rules to a sample data of supermarket transactions. I used the Apriori algorithm to generate frequent itemsets and association rules, and evaluated them using metrics such as support, confidence, and lift. I also visualized the results using plots and graphs.

classification-project

Developed a loan prediction model for the banking and finance domain using various machine learning algorithms, such as logistic regression, KNN, SVM, DT, RF, AB, GB, and XGB. Applied skills such as statistics, EDA, data visualization, machine learning, optimization, and cross-validation using tools such as NumPy, pandas, matplotlib, seaborn.

clustering-project

Implemented various clustering algorithms such as k-means, k-means++, hierarchical clustering, and DBSCAN to segment mall customers based on their spending behavior and demographics.

computer-vision

Real-Time Object Recognition System Using Computer Vision Techniques. I developed an object detection system using OpenCV and Haar Cascade classifiers that can identify various objects in images and videos with high accuracy and speed.

convolution-neural-network

Image Classification Project using Convolutional Neural Network

ford-car-price-prediction-

I developed a model that predicts the price of Ford cars based on various features such as model, transmission, mileage, fuel type, etc. I used different regression models, such as KNN, Decision Tree, Random Forest, AdaBoost, Gradient Boost, and XGBoost, to fit the data and compare their performance.

google-stock-price-prediction-using-rnn-and-lstm-models

Predicted Google stock price using RNN and LSTM models with Keras and visualized the data and the model performance using Matplotlib and Seaborn.

mutual-fund-investment-strategy-optimization-

natural-language-processing

In this project, I used natural language processing techniques to classify SMS messages as spam or ham (not spam) and to discover the topics of the messages.

recommendation-engine-project

Built a movie recommendation engine using TF-IDF vectorizer and cosine similarity on a movies dataset, and provided personalized suggestions based on user input.

regression-project

Built a regression model to predict university admission using linear, polynomial, and regularized regression techniques (lasso, ridge, and elastic net) and achieved 98% accuracy.

shaikayubali

Config files for my GitHub profile.

time-series-and-forecasting

Predicted future passenger demand using time series analysis and ARIMA model on Air Passengers dataset with 96% accuracy, after performing stationarity check and Augmented Dickey-Fuller test.