# First Machine Learning Project in Python Step-By-Step

The best way to learn machine learning in Python by making small projects. Here, we take a small example of the machine learning project of linear regression. Before starting the project let understand machine learning and linear regression.

**Machine learning**

In simple terms, Machine learning is the process in which machines (like a robot, computer) learns the things/algorithms to perform some tasks based on previous experience. Machine learning used in various places for example Tumor detection, Self-driving car, Recommendation system, etc. There are mainly three types of machine learning.

- Supervised learning
- Unsupervised learning
- Reinforcement learning.

**Linear regression**

Linear regression is a technique of supervised learning. It is a statistical approach to find the relationship between variables. Linear regression mostly used for prediction.

**Making project in Machine learning**

Here we make a project of linear regression. We make this project in four steps.

- Implement libraries
- reading the data
- Visualizing the data
- Building the model

**Step – 1 Implementing libraries**

First, we need to import the necessary libraries of Python. Here, we use Pandas, Numpy, Sklearn libraries of python.

Numpy: Numpy is a Python package used for scientific calculation, for example performing different operations on matrix.

Sklearn: Sklearn is a Python package used for performing different machine learning operations, for example predicting the unknown future values.

Pandas: Pandas is a Python package used as a data analysis tool, easy use of data-structure, for example, Dataset can easily be analyzed by the plot.

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score

**Step – 2 Reading the data**

Here use one .csv file with random data. instead of this random data use the dataset.

data=pd.read_csv('Sales.csv') data

Output:

**Step – 3 Visualizing the data**

Describing the data by describe function.

data.describe()

Output:

The graph between sales and month of the year.

plt.scatter(data['Month_of_year'],data['Sales'])

Output:

**Step – 4 Building the model**

Splitting the data in input and target value.

X=data[['Month_of_year']] y=data['Sales']

Making a linear regression model and fitting data into it.

model=LinearRegression() model.fit(X,y)

Prediction by model

y_pred=model.predict(X)

A plot of linear regression.

plt.scatter(X,y) plt.plot(X,y_pred,color='Red')

Output:

Evaluating the model by computing the R square score.

r2_score(y,y_pred)

Output:

Now, we predict the sales for 7.5 months by this model.

print(model.predict([[7.5]]))

Output:

**Dataset **

Dataset used here is a random dataset created by me. You can also use another dataset. You can download a dataset from here: Sales.csv

#### Conclusion

With the help of this small project easily understand the following:

- Machine learning
- Linear regression

For many other projects or other important matters use of Machine learning and linear regression easy way analyze, predict, and get the result with accuracy.

## Leave a Reply