"""
# Predict House Prices Using Machine Learning
"""
"""
![](https://storage.googleapis.com/kaggle-competitions/kaggle/5407/media/housesbanner.png)
"""
"""
# The Challenge 🎯
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
"""
# Import required Python libraries 
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor

# Path of the file to read. We changed the directory structure to simplify submitting to a competition
iowa_file_path = '../input/train.csv'

home_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
"""
Load and Explore Data (EDA)
"""
home_data.describe()
home_data.head()
# Create target object and call it y
y = home_data.SalePrice # Predict Target is sales price 

"""
Create featuers list as X
* LotArea
* YearBuilt
* 1stFlrSF
* 2ndFlrSF
* FullBath
* BedroomAbvGr
* TotRmsAbvGrd



"""
# Create X

features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']

X = home_data[features]

X.head()

# Split into validation and training data
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)

# Specify Model
iowa_model = DecisionTreeRegressor(random_state=1)
# Fit Model
iowa_model.fit(train_X, train_y)

# Make validation predictions and calculate mean absolute error
val_predictions = iowa_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print("Validation MAE when not specifying max_leaf_nodes: {:,.0f}".format(val_mae))

# Using best value for max_leaf_nodes
iowa_model = DecisionTreeRegressor(max_leaf_nodes=100, random_state=1)
iowa_model.fit(train_X, train_y)
val_predictions = iowa_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print("Validation MAE for best value of max_leaf_nodes: {:,.0f}".format(val_mae))

# Define the model. Set random_state to 1
rf_model = RandomForestRegressor(random_state=1)
rf_model.fit(train_X, train_y)
rf_val_predictions = rf_model.predict(val_X)
rf_val_mae = mean_absolute_error(rf_val_predictions, val_y)

print("Validation MAE for Random Forest Model: {:,.0f}".format(rf_val_mae))
"""
# Creating a Model For the Competition
Build a Random Forest model and train it on all of **X** and **y**.
"""
# To improve accuracy, create a new Random Forest model which you will train on all training data
rf_model_on_full_data = RandomForestRegressor(random_state=1)



# fit rf_model_on_full_data on all data from the training data

rf_model_on_full_data.fit(X, y)
"""
# Make Predictions
Read the file of "test" data. And apply your model to make predictions

"""
# path to file you will use for predictions
test_data_path = '../input/house-prices-advanced-regression-techniques/test.csv'

# read test data file using pandas
test_data = pd.read_csv(test_data_path)

# create test_X which comes from test_data but includes only the columns you used for prediction.
# The list of columns is stored in a variable called features
#******************* text x = featuers columns

features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
test_X = test_data[features] 


# make predictions which we will submit. 
test_preds = rf_model_on_full_data.predict(test_X)


output = pd.DataFrame({'Id': test_data.Id,
                       'SalePrice': test_preds})
"""
# Test final data befor submit
"""
output.head()
"""
**Print Full output to make sure rows, cols are corrects**
"""
print(output)

"""
# Submission
"""
"""
Convert data frame output into csv file submission.csv
"""
output.to_csv('submission.csv', index=False) # convert data frame into csv file
"""
Please consider upvoting if you find it useful to you 👍.

Thanks.
"""