Upload First_Version.py

First_Version.py

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+import pandas as pd
+
+df = pd.read_csv("Data_With_Phonks_and_Not_Phonks.csv")
+
+from sklearn.model_selection import train_test_split
+
+train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)
+
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import IterativeImputer
+
+imputer = IterativeImputer(initial_strategy="median", random_state=42)
+
+import numpy as np
+
+training_data_num = train_data.select_dtypes(include=[np.number])
+
+imputer.fit(training_data_num)
+
+X = imputer.transform(training_data_num)
+
+imputer.feature_names_in_
+
+train_data_tr = pd.DataFrame(X, columns=training_data_num.columns,
+                             index=training_data_num.index)
+from sklearn.pipeline import Pipeline
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import IterativeImputer
+from sklearn.preprocessing import StandardScaler
+
+num_pipeline = Pipeline([
+    ("imputer", IterativeImputer(initial_strategy="median")),
+    ("scaler", StandardScaler())
+])
+from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
+
+cat_pipeline = Pipeline([
+    ("ordinal_encoder", OrdinalEncoder()),
+    ("imputer", IterativeImputer(initial_strategy="most_frequent")),
+    ("cat_encoder", OneHotEncoder(sparse_output=False)),
+])
+from sklearn.compose import ColumnTransformer
+
+num_attribs = ["danceability_%", "energy_%", "bpm", "speechiness_%", "acousticness_%",
+               "instrumentalness_%", "liveness_%", "valence_%"]
+cat_attribs = ["key", "mode"]
+
+preprocess_pipeline = ColumnTransformer([
+    ("num", num_pipeline, num_attribs),
+    ("cat", cat_pipeline, cat_attribs),
+])
+X_train = preprocess_pipeline.fit_transform(train_data)
+X_train
+y_train = train_data["genre"]
+from sklearn.svm import SVC
+
+svm_clf = SVC(random_state=42)
+svm_clf.fit(X_train, y_train)
+
+X_test = preprocess_pipeline.transform(test_data)
+y_pred = svm_clf.predict(X_test)
+from sklearn.model_selection import cross_val_score
+
+svm_scores = cross_val_score(svm_clf, X_train, y_train, cv=10)
+svm_scores.mean()