Lab rab1

Lab1/main.py

@@ -1,5 +1,5 @@
 import pandas as pd
-from sklearn.preprocessing import MinMaxScaler, StandardScaler
+from sklearn.preprocessing import MinMaxScaler
 
 df = pd.read_csv("C:/Users/user/PycharmProjects/AILabs/Lab1/spaceship-titanic/train.csv")
 df.info()
@@ -11,33 +11,31 @@
 print(nan_matrix.head(100))
 print(nan_matrix.sum())
 
-nan_matrix = df.isnull()
-
 rood_median = df['RoomService'].median()
 age_median = df['Age'].median()
 food_median = df['FoodCourt'].median()
 shopping_median = df['ShoppingMall'].median()
 vip_mean = df['VIP'].mean()
-spa_mean = df['Spa'].mean()
-vrdeck_mean = df['VRDeck'].mean()
+spa_median = df['Spa'].median()
+vrdeck_median = df['VRDeck'].median()
 cabin_mode = df['Cabin'].mode()[0]
 planet_mode = df['HomePlanet'].mode()[0]
-sleep_mode = df['CryoSleep'].mode()[0]
+sleep_mean = df['CryoSleep'].mean
 destination_mode = df['Destination'].mode()[0]
 name_mode = df['Name'].mode()[0]
 
 df.fillna({'Cabin': cabin_mode}, inplace=True)
 df.fillna({'HomePlanet': planet_mode}, inplace=True)
-df.fillna({'CryoSleep': sleep_mode}, inplace=True)
+df.fillna({'CryoSleep': sleep_mean}, inplace=True)
 df.fillna({'Destination': destination_mode}, inplace=True)
 df.fillna({'Name': name_mode}, inplace=True)
 df.fillna({'RoomService': rood_median}, inplace=True)
 df.fillna({'Age':age_median}, inplace=True)
 df.fillna({'ShoppingMall': shopping_median}, inplace=True)
 df.fillna({'FoodCourt':food_median}, inplace=True)
 df.fillna({'VIP':vip_mean}, inplace=True)
-df.fillna({'Spa':spa_mean}, inplace=True)
-df.fillna({'VRDeck':vrdeck_mean}, inplace=True)
+df.fillna({'Spa':spa_median}, inplace=True)
+df.fillna({'VRDeck':vrdeck_median}, inplace=True)
 
 nan_matrix = df.isnull()
 print(nan_matrix.sum())
@@ -48,18 +46,26 @@
 df['RoomService'] = scaler.fit_transform(df[['RoomService']])
 scaler = MinMaxScaler()
 df['FoodCourt'] = scaler.fit_transform(df[['FoodCourt']])
-scaler = StandardScaler()
+scaler = MinMaxScaler()
 df['ShoppingMall'] = scaler.fit_transform(df[['ShoppingMall']])
-scaler = StandardScaler()
+scaler = MinMaxScaler()
 df['Spa'] = scaler.fit_transform(df[['Spa']])
-scaler = StandardScaler()
+scaler = MinMaxScaler()
 df['VRDeck'] = scaler.fit_transform(df[['VRDeck']])
 
-df = pd.get_dummies(df, columns=['HomePlanet'], drop_first=True)
-df = pd.get_dummies(df, columns=['CryoSleep'], drop_first=True)
-df = pd.get_dummies(df, columns=['Cabin'], drop_first=True)
-df = pd.get_dummies(df, columns=['Destination'], drop_first=True)
-df = pd.get_dummies(df, columns=['VIP'], drop_first=True)
-df = pd.get_dummies(df, columns=['Name'], drop_first=True)
+#df = pd.get_dummies(df, columns=['HomePlanet'], drop_first=True)
+#df = pd.get_dummies(df, columns=['Cabin'], drop_first=True)
+#df = pd.get_dummies(df, columns=['Destination'], drop_first=True)
+#df = pd.get_dummies(df, columns=['Name'], drop_first=True)
+
+df.drop('VRDeck', axis='columns', inplace= True)
+df.drop('ShoppingMall', axis='columns', inplace= True)
+df.drop('FoodCourt', axis='columns', inplace= True)
+df.drop('CryoSleep', axis='columns', inplace= True)
+df.drop('Cabin', axis='columns', inplace= True)
+df.drop('RoomService', axis='columns', inplace= True)
+df.drop('Spa', axis='columns', inplace= True)
+df.drop('Destination', axis='columns', inplace= True)
+df.drop('HomePlanet', axis='columns', inplace= True)
 
 df.to_csv("processed_titanic.csv", index=False)