• Predicting the present value of used cars based on 16 different features.
• Used python libraries for Preprocessing, statistical analysis, manipulation, and applying ML Algorithms to the dataset
• Deployed the Build model using the Django web framework on AWS

• Among the trained model for predicting future sales, a gradient boosting machine with tuned hyperparameters was used for the results.
• Various models are tested for better understanding like linear regression, decision tree, and ridge regression.
• Performed the exploratory data analysis to know the effect of factors on weekly sales & 45 store sales.

Predicting the present value of used cars based on 16 different features. Used python libraries for Preprocessing, statistical analysis, manipulation, and applying ML Algorithms to the dataset Deployed the Build model using the Django web framework on AWS

The analyzed data set of 1.5 million accident records accounting for 49 states of America. Exploratory Data Analysis to identify more accident-prone areas in states. Analyzed data with univariant, bivariant, and Heat Maps of accidents to extract insights from cities based on temperature and area.