Weather prediction using python Machine learning project ( Naïve Bayes )

 This blog contains the code for Weather prediction machine learning project using python Programming

STEP1: Installing Dependencies

*pip install pandas

*pip install sklearn

STEP2: Importing the libraries

#NaiveBayes project (Weather Prediction)

#Required Modules

import pandas as pd

from sklearn.preprocessing import LabelEncoder

from sklearn.naive_bayes import GaussianNB

STEP3:Reading the CSV

df = pd.read_csv("new_dataset.csv")

df

STEP4:Encoding the strings into numbers:

Numerics=LabelEncoder()

STEP5:Dropping the target column

inputs=df.drop('Play',axis='columns')

target=df['Play']

target

STEP6:Creating new dataframe

inputs['outlook_n']= outlook_at.fit_transform(inputs['Outlook'])

inputs['Temp_n']= outlook_at.fit_transform(inputs['Temp'])

inputs['Hum_n']= outlook_at.fit_transform(inputs['Humidity'])

inputs['win_n']= outlook_at.fit_transform(inputs['Windy'])

inputs

STEP7: Dropping the string columns

inputs_n=inputs.drop(['Outlook','Temp','Humidity','Windy'],axis='columns')

inputs_n

STEP8: Applying GaussianNB

classifier = GaussianNB()

classifier.fit(inputs_n,target)

STEP9: Prediction

classifier.predict([[0,0,0,1]])

Video explanation:

                                                               

Fullcode:Github

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