Data Science- Car Price Prediction-Model Building Deployment

Data Science: Car Price Prediction-Model Building Deployment, available at $49.99, has an average rating of 4.45, with 31 lectures, based on 13 reviews, and has 94 subscribers.

You will learn about Data Analysis and Understanding Univariate and Bivariate Analysis Data Preparation Model Building using XGBoost to predict price of a car 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 This course is ideal for individuals who are Students and professionals who want to learn Data Analysis, Data Preparation for model building, Model Creation, Evaluation and model Deployment on Cloud. or Students and professionals who wants to visually interact with their created models. or Professionals who knows how to create models but wants to deploy their models on cloud platform. It is particularly useful for Students and professionals who want to learn Data Analysis, Data Preparation for model building, Model Creation, Evaluation and model Deployment on Cloud. or Students and professionals who wants to visually interact with their created models. or Professionals who knows how to create models but wants to deploy their models on cloud platform.

Enroll now: Data Science: Car Price Prediction-Model Building Deployment

Summary

Title: Data Science: Car Price Prediction-Model Building Deployment

Price: $49.99

Average Rating: 4.45

Number of Lectures: 31

Number of Published Lectures: 31

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: ?999

Quality Status: approved

Status: Live

What You Will Learn

Data Analysis and Understanding

Univariate and Bivariate Analysis

Data Preparation

Model Building using XGBoost to predict price of a car

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

Who Should Attend

Students and professionals who want to learn Data Analysis, Data Preparation for model building, Model Creation, Evaluation and model Deployment on Cloud.

Students and professionals who wants to visually interact with their created models.

Professionals who knows how to create models but wants to deploy their models on cloud platform.

Target Audiences

Students and professionals who want to learn Data Analysis, Data Preparation for model building, Model Creation, Evaluation and model Deployment on Cloud.

Students and professionals who wants to visually interact with their created models.

Professionals who knows how to create models but wants to deploy their models on cloud platform.

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 buildingand evaluationand 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 modeland 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  :  Performing Univariate analysis on variables.

Task 7  :  Performing 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 :  Performing 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.

So what are you waiting for?

Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We’ll see you inside the course!

[Please note that this course and its related contents are for educational purpose only]

Happy Learning !!

Course Curriculum

Chapter 1: Introduction and Getting Started

Lecture 1: Project Overview

Lecture 2: Installing Packages

Chapter 2: Data Understanding, Exploration & Cleaning

Lecture 1: Importing Libraries

Lecture 2: Loading the data from source

Lecture 3: Understanding the data

Lecture 4: Data Cleaning

Chapter 3: Data Analysis

Lecture 1: Performing univariate analysis on variables

Lecture 2: Bivariate analysis on categorical variables

Lecture 3: Data Binning

Lecture 4: Bivariate analysis on numerical variables

Lecture 5: Finding correlation and plotting Heat Map

Lecture 6: Plotting Scatter Plots

Lecture 7: Visualizing Distribution Plots of variables

Lecture 8: Outlier Analysis

Chapter 4: Data Preparation

Lecture 1: Performing One Hot Encoding

Lecture 2: Train Test Split

Lecture 3: Scaling using StandardScaler

Chapter 5: Model Building using XGBoost

Lecture 1: About XGBoost

Lecture 2: Creating XGBoostRegression model with default parameters

Lecture 3: Hyperparameter Tuning using RandomizedSearchCV

Lecture 4: Building XGBRegression model with the selected hyperparameters

Chapter 6: Prediction and Model Evaluation

Lecture 1: Calculating R2 score

Lecture 2: Plotting a scatter plot of the actual and predicted values

Lecture 3: Extracting most important features and its coefficients

Chapter 7: Running the model on a local Server

Lecture 1: What is Streamlit and Installation steps

Lecture 2: Creating an user interface to interact with our created model.

Lecture 3: Running the model on Local Streamlit Server

Chapter 8: Deploying the project on Heroku Platform

Lecture 1: Updating your Project directory

Lecture 2: Pushing your code to Github repository

Lecture 3: Project deployment on Heroku Platform

Chapter 9: Project Files and Code

Lecture 1: Full Project Code

Instructors

AutomationGig .
ELEARNING HUB

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 3 votes

5 stars: 9 votes

Frequently Asked Questions

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