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@MCMXCIII
Created October 19, 2019 18:05
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Regression for multiline
#FOR MULTILINES
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sasbornInstance
from sklearn.model_selection
import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrixcs
%matplotlib inline
dataset = pd.read_csv()
dataset.shape
dataset.describe
#clean null values
dataset.isnull().any()
dataset = dataset.fillna(method='ffill')
#divid the data
X= dataset[[]].values
y= dataset[].values
#Check average values of the column
plt.figure(figsize=(15,10))
plt.tight_layout()
seabornInstance.distplot(dataset['qualitly'])
#spilt the data 80/20 for train/test
X_train,X_test, y_train, y_test = train_test_spilt(X,y, test_size=0.2, random_state=0)
#WE A TRAIN DEM
regressor = LinearRegression()
regressor.fit(X_train,, y_train)
#for multi-line looking for the most effecient CoEf
coeff_df = pd.DataFrame(regressor.coef_,X.columns,columns=['Coefficent'])
coeff_df
#guessing games
y_pred = regressor.predict(X_test)
#check the difference between actual value and predicited value
df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})
df1 = df.head(25)
#plot this
df1.plot(kind='bar' ,figsize=(10,8))
plt.grid(which='major', linestyle='-', linewidth='0.5',color='green')
plt.grid(which='minor'),linestyle='-', linewidth='0.5',color='black')
plt.show()
'''
#evaluate the performance of the algo
print('Mean Absolute Error:', 0.0)
print('Mean squared error':, 0.0)
print('Root Mean Sqaured Error': 0.0)
'''
#Done?
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