• Iris Classification: Developed a machine learning model using Scikit-learn to classify Iris flower species with 93.6% accuracy.• Exploratory Data Analysis on Terrorism Dataset: Conducted comprehensive analysis using Python libraries, uncovering insights into global terrorism trends and patterns.• Handwritten Digit Classification: Designed and trained a convolutional neural network (CNN) using TensorFlow and Keras, achieving 98% accuracy in classifying handwritten digits.

• Stock Price Prediction: Built an LSTM-based deep learning model to predict Google stock prices with high accuracy. Achieved reliable forecasts for 10-day opening and closing prices through advanced data processing, feature engineering, and visualization.• Titanic Survival Prediction: Designed a machine learning model to predict Titanic survival rates, achieving an F-score of 76% and ROC AUC of 93%. Focused on feature selection, data cleaning, and model optimization.

• Predicting Student Scores: Created a linear regression model to predict student scores based on study hours, achieving 92% accuracy. Visualized results using Matplotlib and Seaborn.• Iris Dataset Clustering: Applied k-means clustering on the Iris dataset, using the WCSS method to determine optimal clusters and providing insights into species grouping.