Case Study

Predict Used Car Prices using Linear Regression

The case study is curated to help users understand the concept of a Linear Regression Model to accurately predict prices of used cars

Applying Linear Regression to predict used car prices
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    Difficulty: Advanced

    Designed for those with a technical background or industry experience

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    Duration: Approximately 3 hours

Case Overview

  • This case helps the learner to apply supervised machine learning techniques (Linear Regression) for predicting the price of used cars.
  • Understand key insights about the data by implementing various univariate & bivariate visualization methods.
  • Deep-dive to the concept of data cleaning techniques such as missing value treatment and outlier treatment to improve predictive power of Machine learning models.
  • Working understanding of feature engineering technqiues such as one hot encoding to convert categorical data to numerical data, as most algorithms do not support categorical input.

What’s included

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Access this case study for life once completed

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Flexible Scheduling

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Desktop Only

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Skills You Will Learn

Data understanding and Exploration

Data Cleaning

Data Preparation: Feature Engineering and Scaling

Data Visualization

Data Mining

Machine Learning concepts and Model Building

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Javascript

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