Upload _2146.py

_2146.py

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+# -*- coding: utf-8 -*-
+""".2146
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1zrav0p7dTPU_wC5Hee4bqYFrJU2qMRZw
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings('ignore')
+# %matplotlib inline
+
+file_path = '/content/employment_trends (1).csv'
+df = pd.read_csv(file_path)
+
+df.head()
+
+df['REF_DATE'] = pd.to_datetime(df['REF_DATE'], errors = 'coerce')
+
+missing_values = df.isnull().sum()
+missing_values
+
+sns.histplot(df['VALUE'].dropna(), bins=30, kde=True)
+plt.title('Distribution of Employment Values')
+plt.xlabel('Employment Value')
+plt.ylabel('Frequency')
+plt.show()
+
+plt.figure(figsize=(12, 6))
+sns.countplot(data=df, x='GEO', order=df['GEO'].value_counts().index)
+plt.xticks(rotation=90)
+plt.title('Employment Trends by Geography')
+plt.xlabel('Geography')
+plt.ylabel('Count')
+plt.show()
+
+numeric_df = df.select_dtypes(include=[np.number])
+plt.figure(figsize=(10, 8))
+sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm', fmt='.2f')
+plt.title('Correlation Heatmap')
+plt.show()
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+
+df_model = df.dropna(subset=['VALUE'])
+X = df_model[['UOM_ID', 'SCALAR_ID', 'DECIMALS']]
+y = df_model['VALUE']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+mse = mean_squared_error(y_test, y_pred)
+rmse = np.sqrt(mse)
+rmse