- Naive Bayes Intution
- What is Bayes Formulae
- Implementation Naive Bayes with socail network database
- Please Click Here to download the dataset used for this example

## What is Naive Bayes

In machine learning, naïve Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naïve) independence assumptions between the features. They are among the simplest Bayesian network models. But they could be coupled with Kernel density estimation and achieve higher accuracy levels.

The Naive Bayesian classifier is based on Bayes’ theorem with the independence assumptions between predictors. A Naive Bayesian model is easy to build, with no complicated iterative parameter estimation which makes it particularly useful for very large datasets. Despite its simplicity, the Naive Bayesian classifier often does surprisingly well and is widely used because it often outperforms more sophisticated classification methods.

## Algorithm

Bayes theorem provides a way of calculating the posterior probability, P(c|x), from P(c), P(x), and P(x|c). Naive Bayes classifier assume that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is called class conditional independence.

## Bayes Formulae

```
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
```

```
df = pd.read_csv('Social_Network_Ads.csv')
df.describe()
```

```
df.shape
```

```
df.head(5)
```

```
# here purchased is our dependent variable
```

```
#Converting string values to int so that our model can fit to the dataset better
from sklearn.preprocessing import LabelEncoder
scall = LabelEncoder()
df.iloc[: , 1] = scall.fit_transform(df.iloc[:,1])
df.head(5)
```

```
# Splitting x and y here
x = df.iloc[: , 1:4].values
y = df.iloc[:, 4].values
print(x[:5])
```

```
# train test split
from sklearn.model_selection import train_test_split
x_train , x_test , y_train , y_test = train_test_split(x, y , train_size = 0.8, test_size = 0.2 , random_state = 1)
print(x_train.shape, x_test.shape , y_train.shape)
```

```
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(x_train, y_train)
```

```
#predicting values
y_pred = classifier.predict(x_test)
```

```
from sklearn.metrics import accuracy_score, confusion_matrix
print('accuracy oy model is : ', accuracy_score(y_test, y_pred))
print('Confusion Matrix:','\n', confusion_matrix(y_test, y_pred))
```

```
## we have 86% accuracy in our model
```

```
plt.scatter(x_test[:, 1],y_test,color ='red')
plt.scatter(x_test[:,1],y_pred, color = 'blue')
plt.show()
```

We can clearly see the results the graph The values that are in a different color are predicted wrong rest are right

```
```