Data Science Car Price Prediction-Model Building Deployment

Data Science Car Price Prediction-Model Building Deployment

What you'll learn

Description

A practical hands on Data Science Project on Car Price Prediction - Model Building & Deployment
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.
Please note that this is a hands on class that means I will code and then you will code.
Data Analysis, Model Building and Deployment is one of the most demanded skill of the 21st century. Take the class now, and have a much stronger grasp of data analysis, machine learning and deployment in just a few hours!
What you will learn in this class : 
1. Data Analysis and Understanding
2. Univariate and Bivariate Analysis
3. Data Preparation
4. Model Building using XGBoost to predict price of a car.
5. Model Evaluation
6. Predicting important variables leading to a car price using XGBoost
7. Running the model on a local Streamlit Server
8. Pushing your notebooks and project files to GitHub repository
9. Deploying the project on Heroku Cloud Platform
Who This Class is For :
1. Students and professionals who want to learn Data Analysis, Data Preparation for model building, Model Creation, Evaluation and model Deployment on Cloud.
2. Students and professionals who wants to visually interact with their created models.
3. Professionals who knows how to create models but wants to deploy their models on cloud platform.
You will receive :
1. All the datasets used in the course are in the resources section.
2. 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 and start learning the most demanded skill of the 21st century. We'll see you inside the course!
Happy Learning !!

Overview

Lesson 1:Introduction
Lesson 2:Project Overview
Lesson 3:Installing Packages
Lesson 4:Importing Libraries
Lesson 5:Loading the data from source
Lesson 6:Understanding the data
Lesson 7:Data Cleaning
Lesson 8:Performing univariate analysis on variables
Lesson 9:Bivariate analysis on categorical variables
Lesson 10:Data Binning
Lesson 11:Bivariate analysis on numerical variables
Lesson 12:Finding correlation and plotting Heat Map
Lesson 13:Plotting Scatter Plots
Lesson 14:Visualizing Distribution Plots of variables
Lesson 15:Outlier Analysis
Lesson 16:Performing One Hot Encoding
Lesson 17:Train Test Split
Lesson 18:Scaling using StandardScaler
Lesson 19:About XGBoost
Lesson 20:Creating XGBoostRegression model with default parameters
Lesson 21:Hyperparameter Tuning using RandomizedSearchCV
Lesson 22:Building XGBRegression model with the selected hyperparameters
Lesson 23:Calculating R2 score
Lesson 24:Plotting a scatter plot of the actual and predicted values
Lesson 25:Extracting most important features and its coefficients
Lesson 26:What is Streamlit and Installation steps
Lesson 27:Creating an user interface to interact with our created model
Lesson 28:Running the model on Local Streamlit Server
Lesson 29:Updating your Project directory
Lesson 30:Pushing your code to Github repository
Lesson 31:Project deployment on Heroku Platform