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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

A Practical Tutorial to Simple Linear Regression Using Python

Sriniketh
Sriniketh Last Updated : 21 Oct, 2024
7 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Hello Everyone for this practical Introduction to Machine Learning using Simple Linear Regression. So, let’s get started:

So, let’s get familiarised with the terms to be used:

Machine Learning (ML): ML is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn themselves and improve from the experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to self-learn.

Data Set: A collection of related sets of information that is composed of separate elements but can be manipulated as a unit by a computer.

Data Visualisation: It is a representation of data or information in a graph, chart, or other visual formats which is helpful to conduct analyses such as predictive analysis which can serve as helpful Visualisation to present.

Data Cleaning: It is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.

Supervised Learning:  The model is trained using ‘labeled data’. Datasets are said to contain labels that contain both input and output parameters. To simplify – ‘Data is already tagged with the correct answer’.

Simple Linear Regression: It is a Regression Model that estimates the relationship between the independent variable and the dependent variable using a straight line [y = mx + c], where both the variables should be quantitative.

Models: Those are output by algorithms and are comprised of model data and a prediction algorithm.

Training Model: In supervised learning, an ML Algorithm builds a model by examining many examples and attempting to find a model that minimizes loss and improves prediction accuracy.

These are the few terms used in this article and to get familiar with. Now let’s get started with the analysis and prediction of the model. In this tutorial, I am going to use supervised data and simple linear regression for analysis and prediction. The Ultimate goal is the predict the height of a person provides his age using the trained model to the highest achievable accuracy using available data. I have used the universal favorite programming language for ML i.e. Python to build and train the ML model and Google Colab Environment.

Table of contents

1. Importing Data Set:

The first and foremost thing we need to do is import the dataset. We have various websites which have these datasets to be used by anyone. So similarly let’s get started on how to import the data set that we are going to use in this tutorial.

Simple Linear Regression - Import data

This single line of code helps us fetch the data used for the tutorial from the URL directly.

Dataset <- Click the link to fetch the dataset which is the above-mentioned URL.

2. Visualising the Data:

In this step after importing the data and mounting it with Colab let’s have an overview of the dataset by importing a Module called pandas. Since the dataset we have has an extension of .pkl we just view it by the function available in the pandas library.

We import the library to read the dataset and store it in a variable called raw_data. We then display the content of raw_data which is in a tabulated format.

import pandas as pd
raw_data = pd.read_pickle('AgesAndHeights.pkl')
print(raw_data.head())

We can see the data which we have and it contains only 2 columns namely, Age (in years) and Height (in inches) and 100 rows which is actually the representation of a person.

history

This single line of code has a great impact on the way we look at the dataset. We only had a numerical view of the dataset but we can now run this cell to get a histogram view of the dataset which is very helpful. It represents the data present in the individual columns as individual graphs.

Simple Linear Regression frequency tables

The Y-axis in both the plots refers to frequency and X-axis represents Age and Height respectively.

3. Data Cleaning:

We have to build the model using valid datasets and clean the unaccountable Data. In the above image, we can know that there are a few entries that have an age less than zero which is meaningless. Hence, we need to clean those data to get better accuracy.

Data Cleaning,simple linear regression

I use variable cleaned_data to store the valid age values and display them to the user.

cleaned data

Initially, we had 100 rows but after performing Data Cleaning it’s pretty clear that there are seven rows which we had age < 0 and we have removed them. As a professional, we aren’t supposed to delete the data as we are reducing the data and thereby accuracy of our model gets reduced. To keep it simple I have just removed them.

Visualize the Cleaned Data: I have now used the cleaned data and visualized it in the form of a graph.

simple linear regression clean data code

To plot graphs in python I import matplotlib.pyplot library. I represent Age on X-axis and Height on Y-axis. The points in the plot refer to the Raw data.

Simple linear regression clean data

 

4. Build the Model and Train it:

This is where the ML Algorithm i.e. Simple Linear Regression comes into play.

ML Algorithm i.e. Simple Linear Regression

I used a dictionary named parameters which has alpha and beta as key with 40 and 4 as values respectively. I have also defined a function y_hat which takes age, and params as parameters. This function uses the basic straight-line equation and returns y i.e. height as in our case. If we pass the required parameters and run the function, we find that the height we get for the age as input is not matched. Hence, we use the function mentioned below to rain the model.

Build the Model and Train it

This is where we use a method to find the correct alpha and beta. The function learn_parameters takes cleaned_data and a dummy dictionary new_parameter which can have any value for alpha and beta. So, when we pass them as arguments to parameters and function runs and we can get the correct value of alpha and beta which is found to close to 30 and 2 respectively and replace the old values with the new ones.

new_parameter

We have accurately found the values of alpha and beta, and our next goal is to train the data. But let me the untrained predicted values to what extent they are accurate.

alpha and beta

I use a list named spaces_ages that has values from 0 to 18 (end – 1). Then another list named spaced_untrained_predictions that has the predicted values for the height uses the y_hat function defined earlier to predict it. These values are plotted in a graph and visualized.

spaced_untrained_predictions

The green line shows that the spaced_untrained_predictions have largely deviated from the actual values and the accuracy is very poor. Hence, accuracy needs to be increased for which we need to train the data.

spaced_untrained_predictions

So instead of using parameters, we use new_parameters as it contains the accurate value of alpha and beta and stores it in a list named spaced_trained_predictions. So, when we plot a graph for this, we can see a visible difference and the accuracy has increased a lot. Therefore, we have successfully built and trained the model. Proof for that is the values of spaced_trained_predictions and the graph.

parameters

The Greenline refers to the values of spaced_untrained_predictions and Redline refers to the values of spaced_trained_predictions.

5. Make Predictions on Unseen Data:

With the help of this trained model, we can now make accurate predictions.

 Make Predictions on Unseen Data

So, we can see for any given age we find the possible height in inches. Finally, we have successfully trained the model and with utmost accuracy which is the ultimate goal of this tutorial.

Conclusion

In summary, this hands-on guide to Simple Linear Regression with Python walks you through crucial steps: importing data, visualizing it, cleaning, building, training the model, and making predictions. Mastering these basics will boost your ability to analyze data effectively. Ready to enhance your skills

For reference, I have pasted the Link to Notebook you play around with it. Hope to get connected through LinkedIn as well and share your valuable feedback. Stay tuned for more blogs of this kind😊  .

Frequently Asked Questions

Q1.What is the formula for linear regression in Python?

The formula is y=mx+b, and in Python, use scikit-learn’s LinearRegression class.

Q2.What are the Steps in Simple Linear Regression:

1.Import Libraries:
Use numpy, matplotlib, and scikit-learn.
2.Prepare Data:
Create or load the dataset with X and Y variables.
3.Split Data:
Divide into training and testing sets with train_test_split.
4.Train Model:
Create and train the model using LinearRegression.
5.Make Predictions:
Predict values for test data.
6.Evaluate Model:
Assess performance by comparing predicted and actual values, often with a plot.
7.Use for Predictions:
Apply the model to predict Y values for new X values.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

blogathonlinear regressionsimple linear regression
Sriniketh
Sriniketh

A seasoned software and ML developer with likes to share knowledge on the latest skills, frameworks, and technologies. Writes about Data Science and Machine learning and love to build and ship projects.

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rutik
rutik

can you please explain me the Analyse the performance of the model for AgesAndHeights.pkl

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