Just follow the steps below to analyze the customer data (it’s fake, don’t worry I didn’t give you real credit card numbers or emails).

## Imports

**Import pandas, numpy, matplotlib,and seaborn. Then set %matplotlib inline (You’ll import sklearn as you need it.)**

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
```

## Get the Data

We’ll work with the Ecommerce Customers csv file from the company. It has Customer info, suchas Email, Address, and their color Avatar. Then it also has numerical value columns:

- Avg. Session Length: Average session of in-store style advice sessions.
- Time on App: Average time spent on App in minutes
- Time on Website: Average time spent on Website in minutes
- Length of Membership: How many years the customer has been a member.

**Read in the Ecommerce Customers csv file as a DataFrame called customers.**

`df = pd.read_csv("Ecommerce Customers")`

**Check the head of customers, and check out its info() and describe() methods.**

`df.head()`

```
df.info()
```

```
df.describe()
```

`sns.jointplot(df["Time on Website"],df["Yearly Amount Spent"]);`

**Do the same but with the Time on App column instead.**

`sns.jointplot(df["Time on App"],df["Yearly Amount Spent"]);`

**Create a linear model plot (using seaborn’s lmplot) of Yearly Amount Spent vs. Length of Membership.**

```
sns.lmplot(x=("Length of Membership"),y=("Yearly Amount Spent"),data=df);
```

## Training and Testing Data

Now that we’ve explored the data a bit, let’s go ahead and split the data into training and testing sets. **Set a variable X equal to the numerical features of the customers and a variable y equal to the “Yearly Amount Spent” column.**

```
X = df[['Avg. Session Length', 'Time on App',
'Time on Website', 'Length of Membership']]
y = df["Yearly Amount Spent"]
```

**Use model_selection.train_test_split from sklearn to split the data into training and testing sets. Set test_size=0.3 and random_state=101**

```
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
```

## Training the Model

Now its time to train our model on our training data!

**Import LinearRegression from sklearn.linear_model**

`from sklearn.linear_model import LinearRegression`

**Create an instance of a LinearRegression() model named lm.**

```
lm = LinearRegression()
```

Train/fit lm on the training data.

`lm.fit(X_train,y_train)`

**Print out the coefficients of the model**

`lm.coef_`

## Predicting Test Data

Now that we have fit our model, let’s evaluate its performance by predicting off the test values!

**Use lm.predict() to predict off the X_test set of the data.**

```
predictions = lm.predict(X_test)
```

Create a scatterplot of the real test values versus the predicted values.

```
plt.scatter(y_test,predictions)
plt.xlabel("Real Test Values")
plt.ylabel("Predicted Values");
```

## Evaluating the Model

Let’s evaluate our model performance by calculating the residual sum of squares and the explained variance score (R^2).

**Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error. Refer to the lecture or to Wikipedia for the formulas**

```
from sklearn import metrics
print("MAE", metrics.mean_absolute_error(y_test,predictions))
print("MSE", metrics.mean_squared_error(y_test,predictions))
print("RMSE", np.sqrt(metrics.mean_absolute_error(y_test,predictions)))
```

```
metrics.explained_variance_score(y_test,predictions)
```

## Residuals

You should have gotten a very good model with a good fit. Let’s quickly explore the residuals to make sure everything was okay with our data.

**Plot a histogram of the residuals and make sure it looks normally distributed. Use either seaborn distplot, or just plt.hist().**

`sns.distplot((y_test-predictions));`

Great Job!

Congrats on your contract work! The company loved the insights! Let’s move on.