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DDAVINDER
Last edited May 31, 2019
Created on May 31, 2019

This exploratory data analysis notebook examines automobile features to predict car prices. It uses pandas and numpy to load and manipulate a dataset hosted on IBM Cloud, then applies statistical methods including descriptive statistics, grouping, correlation analysis, and ANOVA. Visualizations reveal patterns between individual characteristics and price, highlighting which attributes most strongly influence the target variable. The analysis culminates in identifying key predictors for car pricing.

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