A company should always set a goal that should be achievable, otherwise, employees will not be able to work to their best potential if they find that the goal set by the company is unachievable. The task of profit prediction for a particular period is the same as setting goals. If you know how much profit you can make with the amount of R&D and marketing you do, then a business can make more than the predicted profit provided the predicted value is achievable. So in this article, I will take you through the task of profit prediction with machine learning using Python.
Profit Prediction with Machine Learning
The profit earned by a company for a particular period depends on several factors like how much time and money a company spends on R&D, marketing and many more. So for predicting the profit of a company for a particular period we need to train a machine learning model with a dataset that contains historical data about the profit generated by the company.
The task of predicting profit is an important task for every business to set an achievable goal. For example, if the business spends $500 on marketing, it can’t expect a profit of $20,000. Likewise, there are many other factors on which the profit of a business depends. A company must therefore set a goal that can be achieved. In the section below, I will walk you through the task of profit prediction with machine learning using Python.
Profit Prediction using Python
The dataset that I am using for the task of profit prediction includes data about the R&D spend, Administration cost, Marketing Spend, State of operation, and the historical profit generated by 50 startups. So let’s start with the task of profit prediction by importing the necessary Python libraries and the dataset:
R&D Spend Administration Marketing Spend State Profit 0 165349.20 136897.80 471784.10 New York 192261.83 1 162597.70 151377.59 443898.53 California 191792.06 2 153441.51 101145.55 407934.54 Florida 191050.39 3 144372.41 118671.85 383199.62 New York 182901.99 4 142107.34 91391.77 366168.42 Florida 166187.94
This data doesn’t contain any missing values so without wasting any time let’s start by having a look at the summary statistics of this data:
R&D Spend Administration Marketing Spend Profit count 50.000000 50.000000 50.000000 50.000000 mean 73721.615600 121344.639600 211025.097800 112012.639200 std 45902.256482 28017.802755 122290.310726 40306.180338 min 0.000000 51283.140000 0.000000 14681.400000 25% 39936.370000 103730.875000 129300.132500 90138.902500 50% 73051.080000 122699.795000 212716.240000 107978.190000 75% 101602.800000 144842.180000 299469.085000 139765.977500 max 165349.200000 182645.560000 471784.100000 192261.830000
Now let’s have a look at the correlation between the features:
sns.heatmap(data.corr(), annot=True) plt.show()
As this task is based on the problem of regression so I will be using the Linear regression algorithm to train the profit prediction model. So let’s prepare the data so that we can fit it into the model:
Now let’s train a linear regression model on this data and have a look at the predicted values:
Predicted Profit 0 126703.027165 1 84894.750816 2 98893.418160 3 46501.708150 4 129128.397344
So this is how we can predict the profit of a company for a particular period by using machine learning algorithms. Such tasks can help a company to set a target that can be achieved. I hope you liked this article on the task of profit prediction with machine learning using Python. Feel free to ask your valuable questions in the comments section below.