import pandas as pd
import pickle
from sklearn.preprocessing import LabelEncoder
data = pd.read_csv('https://raw.githubusercontent.com/dataprofessor/code/master/streamlit/part3/penguins_cleaned.csv')
df=data.copy()
encoder_species=LabelEncoder()
encoder_island=LabelEncoder()
encoder_sex=LabelEncoder()
df['species']=encoder_species.fit_transform(df['species'])
df['island']=encoder_island.fit_transform(df['island'])
df['sex']=encoder_sex.fit_transform(df['sex'])
def encoder_save():
pickle.dump(encoder_species, open('penguins_enc_sp.pkl', 'wb'))
pickle.dump(encoder_island, open('penguins_enc_is.pkl', 'wb'))
pickle.dump(encoder_sex, open('penguins_enc_sx.pkl', 'wb'))
encoder_save()
x=df.drop('species',axis=1)
y=df['species']
from sklearn.ensemble import RandomForestClassifier
model=RandomForestClassifier()
model.fit(x,y)
pickle.dump(model, open('penguins_model.pkl', 'wb'))
print('finished')
# Multiple Linear Regression
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('50_Startups.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X[:, 3] = labelencoder.fit_transform(X[:, 3])
onehotencoder = OneHotEncoder(categorical_features=[3])
X = onehotencoder.fit_transform(X).toarray()
# Avoiding the Dummy Variable Trap
X = X[:, 1:]
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
