Linear Regression With Sales Prediction Project
Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting.
It performs the task to predict a dependent variable value (y) based on a given independent variable (x). So, this regression technique finds out a linear relationship between x (input) and y(output).
The simplest form of a simple linear regression equation with one dependent and one independent variable is represented by:
Similarly for multiple variable
How we can use Linear Regression for predicting expected sales?
Step 1: Dataset Collection
For predicting sales we have to first train our model with the sales data. so we will have to get the dataset of past some years of sales analysis(preferably 10 years) and clean the dataset. you can get a demo dataset from https://github.com/explorewithjag/linear-regression-example/blob/master/data/Advertising_data.csv
Step 2: Data Preprocessing and formatting
Once the right data is selected, preprocessing includes the selection of the right data from the complete dataset and building a training and testing set.
Step 3: Defining and Training Model:
If you are worried about the mathematical calculations that are required for defining the model then relax, thanks to TensorFlow, Keras, and many more Frameworks; they help us in defining the model.
We only have to use their API and define our Model. After defining our model we have to fit our dataset to the model (Train our model) on that dataset.
Step 4: Testing the model
After Training we have to make sure that our model is working fine so we are going to test our model with the test dataset and observe the accuracy. If the accuracy is good then we will save our model for future use.
with this step, the Model Training part is finished. Complete Code — https://github.com/explorewithjag/linear-regression-example/blob/master/simple_and_multiple_linear_regression.ipynb
Step 5: Integrating Model to GUI:
To make our model useful for end-users, we have to integrate our model with a GUI. In this part, we are going to use the Tkinter library of Python for creating a desktop application. In the next part, we will integrate another model with a Flask Website.
Complete Code of GUI — https://github.com/explorewithjag/linear-regression-example/blob/master/integrating_model_with_gui.ipynb
The output of our GUI:
Hope you have enjoyed learning this if so share this with others and for more such contents you can connect with me on
YouTube: https://www.youtube.com/channel/UCmF8qppe02J1ot4Jfwl_lFg
LinkedIn: https://www.dhirubhai.net/in/jagwithyou/
Medium: https://medium.com/@jagwithyou