{"id":177,"date":"2020-06-30T20:24:00","date_gmt":"2020-06-30T20:24:00","guid":{"rendered":"https:\/\/demo.mekshq.com\/johannes\/dc\/?p=177"},"modified":"2023-09-01T16:37:14","modified_gmt":"2023-09-01T16:37:14","slug":"how-can-we-predict-the-profit-of-a-startup","status":"publish","type":"post","link":"https:\/\/amineoumous.com\/blog\/2020\/06\/30\/how-can-we-predict-the-profit-of-a-startup\/","title":{"rendered":"How can we predict the profit of a startup ?"},"content":{"rendered":"\n

Today, we will continue our tutorial on linear regression. As you know, in simple linear regression, we use it when we have a single independent variable and a single dependent variable. However, in multiple linear regression, several independent variables could affect the determination of the dependent variable.<\/p>\n\n\n\n

\"Formula<\/figure><\/div>\n\n\n\n

For example, in simple linear regression, we observed that an employee’s salary depends on the number of years of experience. However, it can also depend on their level of education, skills, and other factors. As you can see, we have several variables influencing the prediction of an employee’s salary, which is why we need to use multiple linear regression.<\/p>\n\n\n\n

Objective<\/h3>\n\n\n\n

We want our model to predict the profit of a startup based on these independent variables in order to help investors determine which companies to invest in, with the goal of maximizing profit.<\/p>\n\n\n\n

Dataset<\/h3>\n\n\n\n

The dataset (can be found here<\/a>) that we use in this model contains data about 50 startups. It has 5 columns: ‘R&D Spend,’ ‘Administration,’ ‘Marketing Spend,’ ‘State,’ and ‘Profit.’ The first 3 columns indicate how much each startup spends on R&D, Marketing, and administration costs, respectively. The ‘State’ column indicates which state the startup is based in, and the last column represents the profit made by the startup.<\/p>\n\n\n\n

Step 1: Import Libraries<\/h3>\n\n\n\n

we will use 4 libraries such as :
– NumPy<\/em>: import numpy as np<\/code>
– Pandas<\/em>: import pandas as pd<\/code> to work with a dataset
– Matplotlib<\/em>: import matplotlib.pyplot as plt<\/code> to visualize our plots for viewing
– Sklearn<\/em>: from sklearn.linear_model import LinearRegression<\/code> to implement machine learning functions, in this example, we use Linear Regression<\/p>\n\n\n\n

Step 2: Load the Dataset<\/h3>\n\n\n\n

We will be using the pandas dataframe. Here, X contains all the independent variable, which are \u201cR&D Spend\u201d, \u201cAdministration\u201d, \u201cMarketing Spend\u201d and \u201cState\u201d. And y is the dependent variable, which is the \u201cProfit\u201d.
So for X, we specify : X = dataset.iloc[:,:-1].values<\/kbd>
and for y, we specify : y = dataset.iloc[:,4].values<\/kbd><\/p>\n\n\n\n

Step 3: Convert text variable to numbers<\/h3>\n\n\n\n

We can see that in our dataset, we have a categorical variable ‘State’ that needs to be encoded. The ‘State’ variable is at index 3. We will use the OneHotEncoder<\/kbd> and ColumnTransformer<\/kbd> classes to convert text to numbers<\/p>\n\n\n\n