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# Car Purchase Decision Classification

Güncelleme tarihi: 1 Haz

In this project we will try to examine the car purchase with our data set which I took from kaggle and we will built a machine learning models with our data set. Data set link

Columns : User ID, Gender, Age, Annual Salary, Purchase Decision (No = 0; Yes = 1)

`df.shape`

We have 1000 rows and 5 columns in our data set

`df.head()` We are not going to use User ID column so we can drop that column

`df = df.drop('User ID', axis ="columns")`

`df["Gender"].value_counts()`

We have 516 female and 484 male in our data set

`df["Purchased"].value_counts()`

598 customers didn't bought the car and 402 bought

`sns.histplot(x = "AnnualSalary", data = df, hue = "Gender")` Seems like female customers earning more salary than male customers in our data set

`sns.histplot(x = "Age", data = df, hue = "Gender")` Female customers are older than male customers in our data set

`sns.histplot(x = "AnnualSalary", data = df, hue = "Purchased")` As it can expected, customers who has higher income more likely to buy the car

`sns.histplot(x = "Age", data = df, hue = "Purchased")` Older customers are more likely to purchase. Now we can get dummy variables for gender column for training our classification models

```df = pd.get_dummies(df, drop_first=False)
df = df.drop("Gender_Male",axis ="columns")```

Now we can get correlation coefficient values on sales and our features

`df.corr()["Purchased"].sort_values()` Age is the most correlated feature with our purchasement and annual salary follows it. It seems like gender is not that correlated with purchasement decision. Lets define our X and Y and split them into train and test set

```X = df[["AnnualSalary","Age","Gender_Female"]].copy()
y = df[["Purchased"]].copy()```

```from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.30,random_state=100)```

We will scale our X sets

```from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_X_train = scaler.fit_transform(X_train)
scaled_X_test = scaler.fit_transform(X_test)```

We will define a function for comparising our models performances in terms of accuracy score

```from sklearn.metrics import accuracy_score
def modelperformance(predictions):
print("Accuracy score in model is {}".format(accuracy_score(y_test,predictions)))```

Now we can start training classification models with logistic regression.

```from sklearn.linear_model import LogisticRegression
log_model = LogisticRegression()
log_model.fit(scaled_X_train,y_train.values.ravel())
log_predictions = log_model.predict(scaled_X_test)
modelperformance(log_predictions)```

Accuracy score in logistic regression is 0.79. Logistic regression performs well in this classification task. Now lets see how K Nearest Neighbors Classification performs. In order to decide we will use elbow method and grid search both and pick the best one from them. We will start with elbow method.

```from sklearn.neighbors import KNeighborsClassifier
test_errors = []
for k in range(1,30):
knn_model = KNeighborsClassifier(n_neighbors=k)
knn_model.fit(scaled_X_train,y_train.values.ravel())
knn_pred = knn_model.predict(scaled_X_test)
test_error_rate = 1 - accuracy_score(y_test,knn_pred)
test_errors.append(test_error_rate)
plt.plot(range(1,30),test_errors)
plt.ylabel("Error Rate")
plt.xlabel("K Neighbors")``` It seems like we get the lowest error with K = 10. Lets train our model with K = 10 and check our accuracy score

```knn_elbow = KNeighborsClassifier(n_neighbors = 10)
knn_elbow.fit(scaled_X_train,y_train.values.ravel())
knn_pred = knn_model.predict(scaled_X_test)
modelperformance(knn_pred)```

Our accuracy score with number of neighbors 10 is 0.91. Our KNN model performed well. Lets train support vector machines classification model

```from sklearn.svm import SVC
svm = SVC()
param_grid_svr = {'C':[0.01,0.1,0.5,1],'kernel':['linear','rbf','poly']}
gridsvr = GridSearchCV(svm,param_grid_svr)
gridsvr.fit(scaled_X_train,y_train.values.ravel())
pred_svr = gridsvr.predict(scaled_X_test)
modelperformance(pred_svr)```

Accuracy score is 0.89. Lets train decision tree classification model

```from sklearn.tree import DecisionTreeClassifier
treemodel = DecisionTreeClassifier()
treemodel.fit(scaled_X_train,y_train.values.ravel())
treepred = treemodel.predict(scaled_X_test)
modelperformance(treepred)```

Accuracy score is 0.87. Lets train random forest classification model

```from sklearn.ensemble import RandomForestClassifier
rfr_model = RandomForestClassifier()
n_estimators = [32,64,128,256]
max_features = [2,3,4]
bootstrap = [True,False]
oob_score = [True,False]
param_grid_rfr = {'n_estimators':n_estimators,'max_features':max_features,'bootstrap':bootstrap,'oob_score':oob_score}
grid_rfr = GridSearchCV(rfr_model,param_grid_rfr)
grid_rfr.fit(scaled_X_train,y_train.values.ravel())
print(grid_rfr.best_params_)

```

And train our model with hyperparameters which we found from grid search

```rfc = RandomForestClassifier(max_features = 3, n_estimators = 256, oob_score=True)
rfc.fit(scaled_X_train,y_train.values.ravel())
predsrfc = rfc.predict(scaled_X_test)
modelperformance(predsrfc)```

Accuracy score is 0.87. Best performing classification model was k-nearest neighbors algorithm with K = 10 which we found with elbow method.

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