PredictionModel.py

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedShuffleSplit
from pandas.plotting import scatter_matrix


housing = pd.read_csv("data.csv")


def split_train_test(data, test_ratio):
    np.random.seed(42)
    shuffled = np.random.permutation(len(data))
    print(shuffled)
    test_set_size = int(len(data) * test_ratio)
    test_indices = shuffled[:test_set_size]
    train_indices = shuffled[test_set_size:]
    return data.iloc[train_indices], data.iloc[test_indices]

train_set, test_set  = train_test_split(housing, test_size=0.2, random_state=42)
print(f"Rows in train set: {len(train_set)}\nRows in test set: {len(test_set)}\n")

# from sklearn.model_selection import KFold
# kf=KFold(n_splits=3)
# for train_index,test_index in kf.split(housing):
#     print(len(train_index),"  ",len(test_index))


split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
for train_index, test_index in split.split(housing, housing['CHAS']):
    strat_train_set = housing.loc[train_index]
    strat_test_set = housing.loc[test_index]


housing = strat_train_set.copy()

# corr_matrix = housing.corr()
# print(corr_matrix)
# corr_matrix['MEDV'].sort_values(ascending=False)


housing["TAXRM"] = housing['TAX']/housing['RM']

housing = strat_train_set.drop("MEDV", axis=1)
housing_labels = strat_train_set["MEDV"].copy()

from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy="median")
imputer.fit(housing)

X = imputer.transform(housing)
housing_tr = pd.DataFrame(X, columns=housing.columns)      #comverting the numpy array type x to the dataFrame


from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
my_pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy="median")),
    ('std_scaler', StandardScaler()),
])


housing_num_tr = my_pipeline.fit_transform(housing)

from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor

# model = DecisionTreeRegressor()
# model= LinearRegression()
model = RandomForestRegressor()
model.fit(housing_num_tr, housing_labels)

some_data = housing.iloc[:5]
some_labels = housing_labels.iloc[:5]

prepared_data = my_pipeline.transform(some_data)
predicted_data=model.predict(prepared_data)

print(predicted_data)
print(list(some_labels))

from sklearn.metrics import mean_squared_error
housing_predictions = model.predict(housing_num_tr)
mse = mean_squared_error(housing_labels, housing_predictions)
rmse = np.sqrt(mse)
print(rmse)



from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, housing_num_tr, housing_labels, scoring="neg_mean_squared_error", cv=10)
rmse_scores = np.sqrt(-scores)
print(rmse_scores)

def print_scores(scores):
    print("Scores:", scores)
    print("Mean: ", scores.mean())
    print("Standard deviation: ", scores.std())

print(print_scores(rmse_scores))

from joblib import dump, load

dump(model, 'RealEstatePricePredictor.joblib')

# TESTING

X_test = strat_test_set.drop("MEDV", axis=1)
Y_test = strat_test_set["MEDV"].copy()
X_test_prepared = my_pipeline.transform(X_test)
final_predictions = model.predict(X_test_prepared)
final_mse = mean_squared_error(Y_test, final_predictions)
final_rmse = np.sqrt(final_mse)
print(final_predictions, list(Y_test))