Profit Prediction with Machine Learning

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:

print(data.describe())
           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()
profit prediction: correlation

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

Summary

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.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
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