Tutorial 3-End To End ML Project With Deployment-Project Problem Statement,EDA And Model Training

Introduction

In this section, Krishnaik introduces the tutorial and explains that it is the third part of an end-to-end machine learning project implementation. He also mentions that he will be discussing a student performance indicator project.

• Krishnaik welcomes viewers to his YouTube channel and introduces the tutorial.

• He discusses the previous tutorial where they implemented exceptional handling logger for the entire project.

• Krishnaik explains that in the last example, they were trying to raise an exception but it was not getting stored in the file because he forgot to import logging from SRC.logger.

• He imports logging from SRC.logger and shows how to save error messages in a log file.

Project Overview

In this section, Krishnaik talks about the student performance indicator project and why he chose it as a starter project.

• Krishnaik explains that when developing an end-to-end project, choosing a dataset is crucial. He chose a student performance indicator dataset because it has categorical features, numerical features, nand values, and other complexities.

• He mentions that this is just their first project and they will continue with more projects related to NLP deep learning projects.

• Krishnaik says that they will try to make each project better by implementing new techniques or improving performance through ML ops CI CD pipeline.

• He emphasizes that anyone who follows along with these tutorials can crack any interview related to machine learning or deep learning projects.

• Krishnaik notes that some prerequisites are required for following along with these tutorials such as knowing Python programming language, modular coding, EDA, etc.

Student Performance Indicator Dataset

In this section, Krishnaik talks about the student performance indicator dataset and why it is a good starter project.

• Krishnaik shows the student performance indicator dataset and explains that it has categorical features, numerical features, nand values, and other complexities.

• He notes that this project will start with feature engineering to solve these complexities.

Introduction to the Project

In this section, the instructor introduces the project called "Student Performance Indicator" and explains the folder structure of the project.

Project Overview

• The project is called "Student Performance Indicator".

• The project involves understanding how student performance test scores are affected by variables such as gender, ethnicity, parental level of education, lunchtime preparation course, etc.

• The dataset consists of 1000 rows and 8 columns.

• The folder structure includes a notebook folder with data files and two other files for EDA and model training.

Understanding Exploratory Data Analysis (EDA)

In this section, the instructor explains what EDA is and why it's important in a machine learning project.

Importance of EDA

• Observation is super important when performing EDA.

• Every step in a machine learning project should have a reason.

• EDA helps to understand how students perform with respect to test scores based on different features.

• Categorical and numerical features will be analyzed during EDA.

Using Jupyter Notebook for EDA

In this section, the instructor explains why Jupyter Notebook is the best way to perform EDA.

Benefits of Using Jupyter Notebook for EDA

• Jupiter Notebook is the best way to perform exploratory data analysis (EDA).

• Observations made during EDA are very important.

• Stakeholders need to be provided with observations made during each step of a machine learning project.

Lifecycle of Machine Learning Project

In this section, the instructor explains the lifecycle of a machine learning project.

Lifecycle Stages

1. Understanding the problem statement

2. Data collection

3. Data checks

4. Perform exploratory data analysis (EDA)

5. Data pre-processing

6. Model training

7. Choosing the best model

8. Evaluating the model

9. Pushing the model

Problem Statement

In this section, the instructor explains the problem statement of the project.

Problem Statement

• The project aims to understand how student performance test scores are affected by variables such as gender, ethnicity, parental level of education, lunchtime preparation course, etc.

• The dataset consists of 1000 rows and 8 columns.

Promise of Learning

In this section, the instructor promises that students will be able to implement any Python or machine learning project after completing this project.

Promise of Learning

• Students will be able to implement any Python or machine learning project after completing this project.

• The goal is to help students gain a comprehensive understanding of how to implement an end-to-end machine learning project.

Importing Libraries and Selecting Python Kernel

In this section, the instructor imports libraries and selects a Python kernel for executing code in Jupyter Notebook.

Importing Libraries and Selecting Python Kernel

• Libraries such as Matplotlib are imported.

• A Python kernel is selected for executing code in Jupyter Notebook.

Folder Structure

In this section, the instructor explains the folder structure used in the project.

Folder Structure

• The notebook folder contains data files and two other files for EDA and model training.

• Analysis and model training will be done in their respective folders.

Introduction to the Dataset

In this section, the presenter introduces the dataset and explains how to install packages.

Installing Packages

• To install a package, use "pip install -r requirement.txt"

• The presenter opens EDM to read the dataset.

Dataset Information

• The dataset contains information on gender, math score, reading score, and writing score.

• Data checks include missing values, duplicate values, data type, number of unique values in each column, statistics of the dataset and categories present in different category columns.

Missing Values and Duplicates

• Check for missing values and duplicates.

• If there are no missing values but duplicates exist, remove them.

• If there are missing values with feature engineering replace them.

Exploring the Dataset

In this section, we explore more about the dataset by checking its shape and statistics.

Checking Shape of Dataset

• Check for shape of dataset using "DF.unique()".

• Gender has two categories while parental level of education has six categories.

Checking Statistics of Dataset

• Use "DF.describe()" to check statistics of numerical features.

• All means are very close to each other.

Insights from Dataset Exploration

In this section, we look at insights gained from exploring the dataset.

Insights Gained from Exploration

• Write down insights gained from exploration such as all means being very close to each other.

• Suggest going through each point carefully as it will be useful later when converting into modular coding.

More Exploration on Categories in Dataset

In this section we explore more about categorical features in our dataset.

Categorical Features in Dataset

• Explore categorical features such as race ethnicity and test preparation courses.

• Define numerical and categorical features.

Introduction to Feature Engineering

In this section, the speaker introduces feature engineering and explains how to identify numerical and categorical features in a dataset.

Identifying Numerical and Categorical Features

• If a feature is equal to zero or an object, it is considered a categorical feature.

• To find the number of unique categories in a categorical feature, use DF[column_name].unique().

• The dataset used has three numerical features (math score, reading score, writing score) and five categorical features (gender, ethnicity, parental level of education, lunch, test preparation course).

Creating Total Score and Average Score Features

• The speaker creates two new features - total score and average score - by adding up the scores for each student.

• These new features can be used as dependent variables in predictive models.

Exploring Data Insights

In this section, the speaker explores insights that can be gained from analyzing the data.

Finding Students with Full Marks

• The speaker uses conditional statements to find out how many students scored full marks in math, reading, and writing.

Analyzing Performance by Gender

• The speaker uses histograms to analyze average scores based on gender.

• This analysis can provide insights into which gender performs better in certain subjects.

Exploring the Data

In this section, the speaker discusses the data and provides insights on how female students tend to perform better than male students.

Female Students Perform Better

• The average score is increasing with respect to female students.

• Female students tend to perform well in total score as well.

• The insight is that female students tend to perform better than male students.

EDA and Plots

• Check out the graphs for performance with respect to lunch.

• Detailed notes were taken by the team during EDA and plot creation.

• Take sufficient time to understand the project from here.

Model Training

In this section, the speaker discusses model training and handling category features.

Handling Category Features

• The file being used will train a model.

• An error occurs due to a missing library (sk1).

• Install libraries using requirements.txt file.

• Install CAD boost library.

Installing Libraries

• All necessary libraries are installed successfully.

• Error still persists due to missing CAD boost model.

Conclusion

The speaker provides detailed instructions on installing necessary libraries for model training.

Introduction to Libraries and Reading Data

In this section, the instructor introduces the libraries required for the project and reads in data from a CSV file.

Installing Required Libraries

• The instructor checks if pip is installed and installs necessary libraries using pip install.

• Libraries installed include cadboost, Cash boost, XG boost, and SK learn dot matrix.

Reading Data from CSV File

• The instructor reads in data from a CSV file called "student.csv" using pandas.

• The dataframe is displayed using df.head().

Preprocessing Data

In this section, the instructor preprocesses the data by creating independent and dependent variables, performing one-hot encoding on categorical features, and creating a pipeline.

Creating Independent and Dependent Variables

• The instructor creates an independent variable by dropping the math score column from the dataframe.

• The dependent variable is set to be the math score column.

One-Hot Encoding Categorical Features

• The instructor performs one-hot encoding on categorical features using ColumnTransformer with three types of transformers.

• Numerical features are not encoded but checked for unique categories before proceeding with encoding.

Creating Pipeline

• A pipeline is created to perform different transformations on numerical and categorical features.

Column Transformer

In this section, the speaker explains how to use a column transformer to transform columns or data points.

Initializing Standard Scalar and One Hot Encoder

• Initialize standard scalar and one hot encoder.

• Combine them as a pipeline using column transformer.

Applying Column Transformer

• Apply column transformer to transform columns or data points.

• Use fit and transform on any kind of dataset.

Train Test Split

• Create train test split using model selection from sklearn.

• 800 records in training set and 200 records in test set.

Evaluate Model Performance

• Create an evaluation matrix to give metrics about the model training.

• Calculate mean absolute error, mean squared error, root mean square error, and R square values.

• Append all these values to the model list.

Linear Regression for Model Prediction

In this section, the speaker discusses using linear regression for model prediction and demonstrates how to evaluate the accuracy of the model.

Using Linear Regression for Model Prediction

• The speaker explains that they will be using linear regression for model prediction because there is hardly any difference between different models.

• They demonstrate how to evaluate the accuracy of the model by predicting values and comparing them to actual values.

• The predicted value, actual value, and difference are shown in a simple format.

Writing Code for Model Training and Evaluation

• The speaker explains that in the upcoming tutorial, they will write code for model training and evaluation in a modular way.

• They discuss creating functions in a utils.py file to handle tasks such as data ingestion, train-test splitting, column transformation, and evaluation metrics.

• The goal is to map out where each piece of code should go within the overall structure of the program.

Committing Code Changes

• The speaker commits their code changes using Git commands such as "git add" and "git commit".

• They push their changes to GitHub so that others can access their work.

Conclusion

In this final section, the speaker summarizes what was covered in the video and encourages viewers to share it with others who may find it helpful.

Summary

• The speaker recaps what was covered in the video: problem statement, EDA (exploratory data analysis), model training, and modular coding.

• They emphasize that going forward, they will focus on writing modular code.

Encouragement to Share Video

• The speaker encourages viewers to share the video with others who may find it helpful.

• Sharing resources can be important for those who are struggling to learn something new.