What you'll learn

Data Analysis and Understanding

Univariate and Bivariate Analysis

Data Preparation

Model Evaluation

Predicting important variables leading to a car price using XGBoost

Running the model on a local Streamlit Server

Pushing your notebooks and project files to GitHub repository

Deploying the project on Heroku Cloud Platform

Description

This course is about predicting the price of a car based on its features using Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.

This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building and evaluation and deployment techniques. We will use XGBoost algorithm to create our model which helps us in predicting price of a car given its features.

At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.

I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Task 1 : Installing Packages.

Task 2 : Importing Libraries.

Task 3 : Loading the data from source.

Task 4 : Data Understanding

Task 5 : Data Cleaning

Task 6 : Perfog Univariate analysis on variables.

Task 7 : Perfog Bivariate analysis on variables.

Task 8 : Data binning to convert numerical variables to categorical variables.

Task 9 : Finding correlations among features and plotting on HeatMap.

Task 10 : Plotting scatter plots.

Task 11 : Visualizing the distribution of data across variables.

Task 12 : Outlier Analysis.

Task 13 : Perfog One Hot Encoding to convert categorical features to numeric features.

Task 14 : Train Test Split.

Task 15 : Scaling the variables using StandardScaler.

Task 16 : Creating a XGBoostRegression model with default parameters.

Task 17 : Hyperparameter Tuning using RandomizedSearchCV.

Task 18 : Building XGBRegression model with the selected hyperparameters.

Task 19 : Model Evaluation - Calculating R2 score

Task 20 : Model Evaluation - Plotting a scatter plot of the actual and predicted values.

Task 21 : Extracting most important features and its coefficients.

Task 22 : What is Streamlit and Installation steps.

Task 23 : Creating an user interface to interact with our created model.

Task 24 : How to run your notebook on Streamlit Server in your local machine.

Task 25 : Pushing your project to GitHub repository.

Task 26 : Project Deployment on Heroku Platform for free.

Data Analysis, Model Building and Deployment is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of data analysis, machine learning and deployment in just a few hours!

You will receive :

1. Certificate of completion from AutomationGig.

2. All the datasets used in the course are in the resources section.

3. The Jupyter notebook are provided at the end of the course in the resource section.




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