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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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Step6:  Let us look at a typical row from our features
Step7:  Let us also display our target values
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Python Code:
import sqlite3
import pandas as pd 
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from math import sqrt
Explanation: <p style="font-family: Arial; font-size:2.75em;color:purple; font-style:bold"><br>
Regresssion with scikit-learn<br><br> using Soccer Dataset
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We will again be using the open dataset from the popular site <a href="https://www.kaggle.com">Kaggle</a> that we used in Week 1 for our example. 
Recall that this <a href="https://www.kaggle.com/hugomathien/soccer">European Soccer Database</a> has more than 25,000 matches and more than 10,000 players for European professional soccer seasons from 2008 to 2016. 
Note: Please download the file database.sqlite if you don't yet have it in your Week-7-MachineLearning folder.
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Import Libraries<br><br></p>
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# Create your connection.
cnx = sqlite3.connect('database.sqlite')
df = pd.read_sql_query("SELECT * FROM Player_Attributes", cnx)
df.head()
df.shape
df.columns
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Read Data from the Database into pandas
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features = [
       'potential', 'crossing', 'finishing', 'heading_accuracy',
       'short_passing', 'volleys', 'dribbling', 'curve', 'free_kick_accuracy',
       'long_passing', 'ball_control', 'acceleration', 'sprint_speed',
       'agility', 'reactions', 'balance', 'shot_power', 'jumping', 'stamina',
       'strength', 'long_shots', 'aggression', 'interceptions', 'positioning',
       'vision', 'penalties', 'marking', 'standing_tackle', 'sliding_tackle',
       'gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning',
       'gk_reflexes']
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Declare the Columns You Want to Use as Features
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target = ['overall_rating']
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Specify the Prediction Target
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df = df.dropna()
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Clean the Data<br><br></p>
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X = df[features]
y = df[target]
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Extract Features and Target ('overall_rating') Values into Separate Dataframes
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X.iloc[2]
Explanation: Let us look at a typical row from our features:
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y
Explanation: Let us also display our target values:
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=324)
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Split the Dataset into Training and Test Datasets
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regressor = LinearRegression()
regressor.fit(X_train, y_train)
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
(1) Linear Regression: Fit a model to the training set
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y_prediction = regressor.predict(X_test)
y_prediction
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Perform Prediction using Linear Regression Model
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y_test.describe()
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
What is the mean of the expected target value in test set ?
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RMSE = sqrt(mean_squared_error(y_true = y_test, y_pred = y_prediction))
print(RMSE)
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Evaluate Linear Regression Accuracy using Root Mean Square Error
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regressor = DecisionTreeRegressor(max_depth=20)
regressor.fit(X_train, y_train)
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
(2) Decision Tree Regressor: Fit a new regression model to the training set
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y_prediction = regressor.predict(X_test)
y_prediction
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Perform Prediction using Decision Tree Regressor
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y_test.describe()
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
For comparision: What is the mean of the expected target value in test set ?
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RMSE = sqrt(mean_squared_error(y_true = y_test, y_pred = y_prediction))
print(RMSE)
Explanation: <p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Evaluate Decision Tree Regression Accuracy using Root Mean Square Error
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