diff --git a/ecommerce b/ecommerce
new file mode 100644
index 0000000..9a9ecb2
--- /dev/null
+++ b/ecommerce
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+# Import the libraries
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+import math
+import pylab
+import scipy.stats as stats
+
+df = pd.read_csv("/Users/tikkybamiro/Documents/linear practice/ecommerce.csv")
+df.head()
+df.describe()
+
+lm = LinearRegression()
+
+X = df[['Avg. Session Length', 'Time on App', 'Time on Website', 'Length of Membership']]
+Y = df['Yearly Amount Spent']
+
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42)
+
+lm.fit(X_train, Y_train)
+
+cdf = pd.DataFrame(lm.coef_, X.columns, columns=['Coefficients'])
+cdf
+
+predictions = lm.predict(X_test)
+
+sns.scatterplot(x = predictions, y= Y_test)
+plt.xlabel("Predictions")
+
+print("Mean Absolute Error: ", mean_absolute_error(Y_test, predictions))
+print("Mean Squared Error: ", mean_squared_error(Y_test, predictions))
+print("RMSE: ", math.sqrt(mean_squared_error(Y_test, predictions)))
+
+residuals = Y_test - predictions
+
+sns.displot(residuals, bins=20, kde=True)
+plt.ylabel("Frequency")
+plt.xlabel("Residuals")
+
+stats.probplot(residuals, dist="norm", plot=pylab)
+pylab.show()