Prediction-with-Multiple-Regression

In this project, we will be predicting the profit from the startup’s dataset with the features available to us. We’re using the 50-startups dataset for this problem statement and we will be using the concept of Multiple linear regression to predict the profit of startups companies. This machine learning model will be quite helpful in such a situation where we need to find a profit based on how much we are spending in the market and for the market. In a nutshell, this machine learning model will help to find out the profit based on the amount which we spend from the 50 startups dataset. This particular dataset holds data from 50 startups in New York, California, and Florida. The features in this dataset are R&D spending, Administration Spending, Marketing Spending, and location features, while the target variable is: Profit. we will be following steps in our project: Importing libraries, Analyzing the data, EDA on the dataset, Data Visualization, Feature exploration, Model development, Model evaluation The R2 score of the model is: 0.8684673521504285 and The Root Mean Squared Error is: 36.26742999573743 The mean absolute error is 0.2594651474465035. Therefore, our predicted value can be 0.2594651474465035 units more or less than the actual value. By using Multiple linear regression and Exploratory Data Analysis we trained the model in machine learning and visualized all the predictions and data in the form of graphs. For that we used inputs like R&D spend, Administrative spend, and market spend etc. Hence, we could successfully predict the profit of startups.