here is a post about linear regression, the first step of machine learning. we'll be using python to predict divorce cases for mauritius, my country

## What Is Linear Regression

regression just means prediction.

read more on those two dev.to articles

## Our Data

we'll be downloading our csv from here . the first one. i renamed it divorce.csv

## Opening Up Jupyter

let us import our libs

```
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import linear_model
# psst inspect sklearn to see what other models there are
```

let's have a look at our data

```
df = pd.read_csv('<path-to-file>/divorce.csv')
df
```

gives us

we'll be predicting the column *Number of cases disposed of which Divorce pronounced*

but let us have a look at our data

```
plt.scatter(df['Year'], df['Number of cases disposed of which Divorce pronounced'])
```

we get

let us train our model

```
reg = linear_model.LinearRegression()
reg.fit(df[['Year']], df['Number of cases disposed of which Divorce pronounced'])
```

and predict for the year 2017

```
reg.predict([[2017]])# it was 1,921
```

we get

```
array([ 2140.22222222])
```

## Getting m And c

since linear regression is just a straight line (really that whole machine learning world is just some maths), we can get the coefficient (our m) and the intercept (our c)

coefficient is given by

```
reg.coef_
```

outputs

```
array([ 57.79118774])
```

and intercept by

```
reg.intercept_
```

outputs

```
-114424.60344827584
```

## Building Our Own Predictor Function

we can now build a simple function to predict without passing by ml

```
def devdotto_predict(year_):
# m * x + c
return 57.79118774 * year_ + -114424.60344827584
```

and use it

```
devdotto_predict(2017)
```

we get

```
2140.222223304154
```

compare above

## Conclusion

machine learning is super easy if you understand the concept!

cover img credit : Photo by Xavier Coiffic On Unsplash

real pic of mauritius

## Discussion