Ml Lab 1 Linear Regression Github

This repo contains Lab practices of Machine Learning performed in Jupyter Notebook using Python language 🚀 LAB-3: Part-A: Simple Linear Regression ◾ LAB 3 Part A ipynb file consists of the basic python coding required to perform the Simple Linear regression on a given data set and visualize the results. This code files uses the Salary_Data.csv dataset file to perform the Simple Linear regression and Visualizes the results using matplotlib and seaborn libraries. 🚀 LAB-3: Part-B: Multiple Linear Regression ◾ LAB 3 Part B ipynb file consists of the basic python coding required to perform the Multiple Linear regression on a given data set and visualize the results.

This code files uses the 50_Startups.csv dataset file to perform the Multiple Linear regression and Visualizes the results using matplotlib and seaborn libraries. The NO2 dataset contains 500 measurement of pollution caused by cars. The goal is to predict the concentration of \(NO_2\) from data about traffic and atmospheric conditions. The predictive variables include the number of cars per hour, temperature, wind, and time of day. We can use pandas to quickly visualize the data. If you are new to pandas, take some time to understand the code.

We’ll remove the ‘day’ feature to focus on the non-temporal aspects of this interaction. We are not aiming to predict future levels, and even if we would it would require special treatment (e.g. different train-test splits). There also doesn’t seem to be a long term trend in the data, even though there are clear periodic trends in temperature. If we plot the data, ordered by time of measurement, we can see that the wind direction (measured in angular degrees) is scaled very differently from the other features. Let’s now zoom in to the other measures:

We can see that the target (\(NO_2\) levels) seem to be correlated to the number of cars per hour, which makes sense because cars produce \(NO_2\). Other influences (air temperature differences and wind) seem to have a more complex and subtle effect. Let’s try to model these using linear regression models. There was an error while loading. Please reload this page. The NO2 dataset contains 500 measurement of pollution caused by cars.

The goal is to predict the concentration of \(NO_2\) from data about traffic and atmospheric conditions. The predictive variables include the number of cars per hour, temperature, wind, and time of day. We can use pandas to quickly visualize the data. If you are new to pandas, take some time to understand the code. We’ll remove the ‘day’ feature to focus on the non-temporal aspects of this interaction. We are not aiming to predict future levels, and even if we would it would require special treatment (e.g.

different train-test splits). There also doesn’t seem to be a long term trend in the data, even though there are clear periodic trends in temperature. If we plot the data, ordered by time of measurement, we can see that the wind direction (measured in angular degrees) is scaled very differently from the other features. Let’s now zoom in to the other measures: We can see that the target (\(NO_2\) levels) seem to be correlated to the number of cars per hour, which makes sense because cars produce \(NO_2\). Other influences (air temperature differences and wind) seem to have a more complex and subtle effect.

Let’s try to model these using linear regression models. There was an error while loading. Please reload this page. Linear regression is a type of supervised machine-learning algorithm that learns from the labelled datasets and maps the data points with most optimized linear functions which can be used for prediction on new datasets. It assumes that there is a linear relationship between the input and output, meaning the output changes at a constant rate as the input changes. This relationship is represented by a straight line.

For example we want to predict a student's exam score based on how many hours they studied. We observe that as students study more hours, their scores go up. In the example of predicting exam scores based on hours studied. Here We use the independent variable to predict the dependent variable. Here’s why linear regression is important:

In linear regression, the best-fit line is the straight line that most accurately represents the relationship between the independent variable (input) and the dependent variable (output). It is the line that minimizes the difference between the actual data points and the predicted values from the model. Instantly share code, notes, and snippets.

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