Upload application20.py

application20.py

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+import pandas as pd
+df = pd.read_csv('C:/Users/Donte Patton/Downloads/dataset_2191_sleep.csv')
+df.head()
+import warnings
+warnings.filterwarnings('ignore')
+
+print(df.shape)
+
+df.isnull().sum().sum()
+
+df.isnull().sum()
+
+df.dtypes
+
+import pandas as pd
+import numpy as np
+
+df.replace('?', np.nan, inplace=True)
+
+df['max_life_span'] = pd.to_numeric(df['max_life_span'], errors='coerce')
+df['gestation_time'] = pd.to_numeric(df['gestation_time'], errors='coerce')
+df['total_sleep'] = pd.to_numeric(df['total_sleep'], errors='coerce')
+
+print(df.info())
+
+df.describe()
+
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+sns.pairplot(df)
+plt.show()
+
+print(df["body_weight"].describe())
+
+sns.scatterplot(data=df, x="body_weight", y="total_sleep")
+plt.show()
+
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+Q1 = df["body_weight"].quantile(0.25)
+Q3 = df["body_weight"].quantile(0.75)
+IQR = Q3 - Q1
+
+lower_bound = Q1 - 1.5 * IQR
+upper_bound = Q3 + 1.5 * IQR
+
+print(f"Lower bound: {lower_bound}")
+print(f"Upper bound: {upper_bound}")
+
+outliers = df[(df["body_weight"] < lower_bound) | (df["body_weight"] > upper_bound)]
+print("\n Outliers:")
+print(outliers)
+
+filtered_df = df[(df["body_weight"] >= lower_bound) & (df["body_weight"] <=upperbound)]
+
+sns.scatterplot(data=filtered_df, x="body_weight", y="total_sleep")
+plt.title("Scatterplot without Outliers")
+plt.xlabel("Body Weight")
+plt.ylabel("Total Sleep")
+plt.grid(True)
+plt.show()
+
+print(f"\nOriginal row count: {len(df)}")
+print(f"Filtered row count: {len(filtered_df)}")
+
+from sklearn.model_selection import train_test_split
+
+X = df.drop(columns='total_sleep')
+y = df['total_sleep']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_sixe=0.2, random_state=42)