Link to the previous post : https://statinfer.com/204-2-1-logistic-regression-why-do-we-need-it/

In the last post we saw linear regression cannot be used if the final output is binary, yes or no. As it’s tough to fit a binary output on a linear function.

To solve this problem we can move toward some different kind of functions, a Logistic Function being the first choice.

This is how a Logistic Function look like:

- We want a model that predicts probabilities between 0 and 1, that is, S-shaped.
- There are lots of s-shaped curves. We use the logistic model:
Probability=e(β0+β1X)1+e($β0+β1X)

- In logistic regression, we try to predict the probability instead of direct values.
- Y is binary, it takes only two values 1 and 0 instead of predicting 1 or 0 we predict the probability of 1 and probability of zero.
- This suits aptly for the binary categorical outputs like YES vs NO; WIN vs LOSS; Fraud vs Non Fraud.

- Dataset: Product Sales Data/Product_sales.csv
- Build a logistic Regression line between Age and buying
- A 4 years old customer, will he buy the product?
- If Age is 105 then will that customer buy the product?

In [8]:

```
import pandas as pd
sales=pd.read_csv("datasets\\Product Sales Data\\Product_sales.csv")
import statsmodels.formula.api as sm
# Build a logistic Regression line between Age and buying
logit=sm.Logit(sales['Bought'],sales['Age'])
logit
```

Out[8]:

In [9]:

```
result = logit.fit()
result
```

Out[9]:

In [10]:

```
result.summary()
```

Out[10]:

In [11]:

```
###coefficients Interval of each coefficient
print (result.conf_int())
```

In [12]:

```
#One more way of fitting the model
from sklearn.linear_model import LogisticRegression
logistic = LogisticRegression()
logistic.fit(sales[["Age"]],sales["Bought"])
```

Out[12]:

In [13]:

```
#A 4 years old customer, will he buy the product?
age1=4
predict_age1=logistic.predict(age1)
print(predict_age1)
```

In [14]:

```
#If Age is 105 then will that customer buy the product?
age2=105
predict_age2=logistic.predict(age2)
print(predict_age2)
```