design and implementation of a Deep Learning-based system for predicting crop diseases, Developed and trained CNNs and Pretrained models on extensive datasets of crop images to accurately identify and classify diseases, potentially improving agricultural practices and yield optimization. 

This project aims to provide in-depth analysis of the Android app market by gathering and analyzing data on app categories and features. Through this analysis, i will identify factors that contribute to the success of certain apps and determine what it takes for an app to top the charts. my insights will provide valuable information for developers and stakeholders in the mobile app industry. 

This project aims to analyze the occurrence of heart disease using a combination of features that describe the condition. i will utilize various graphs and visualization techniques to explore the data and draw appropriate conclusions. By analyzing this health and medical data, we can better prepare for future occurrences of heart disease and potentially improve preventative measures. my goal is to provide valuable insights for medical professionals and researchers to aid in the fight against heart disease. 

This project involves using Python to convert an image into a pencil sketch. This is done by reading the image in RGB format and converting it to grayscale. The grayscale image is then inverted to enhance details. Finally, the pencil sketch is created by mixing the grayscale image with the inverted blurry image using the divide function from the cv2 library. By using this method, i can easily transform any image into a pencil sketch programmatically. 

This project develops a music recommendation system based on user listening habits. Exploratory data analysis and data cleaning are performed on the dataset. The model is built using LightGBM and logistic regression algorithms. The accuracy of the model on test data is evaluated. The project showcases the effectiveness of the recommendation system in providing personalized music suggestions.

This project analyzes global terrorism data, focusing on attack patterns, regions, and terrorist groups. It reveals that Iraq, specifically Baghdad, has the highest number of attacks. The Middle East and North Africa region are most affected, with 2014 standing out as a peak year. The Taliban and ISIL are prominent terrorist groups. The most common attack type is bombing/explosion. The project provides valuable insights into global terrorism trends and patterns.