Upload 1040_249_949.py

1040_249_949.py

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+# -*- coding: utf-8 -*-
+"""1040_249_949
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1T8VCDZs5tRg-mTI4qNqCct_92fcd_7Rl
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import warnings as w
+w.filterwarnings('ignore')
+
+df=pd.read_csv('//content/1000_ml_jobs_us.csv')
+
+df.head()
+
+df.isnull().sum()
+
+df.drop(columns=['company_website', 'company_description', 'job_description_text', 'Unnamed: 0'], inplace=True)
+
+df['company_address_locality'] = df['company_address_locality'].fillna(df['company_address_locality'].mode()[0])
+df['company_address_region'] = df['company_address_region'].fillna(df['company_address_region'].mode()[0])
+df['seniority_level'] = (df['seniority_level'].fillna(df['seniority_level']).mode()[0])
+
+df.info()
+
+df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])
+
+df['company_address_locality'].value_counts().head(10).plot(kind='bar', title='Top 10 Localities')
+
+df['company_address_region'].value_counts().head(10).plot(kind='bar', title='Top 10 Regions')
+
+df['company_name'].value_counts().head(10).plot(kind='barh', title='Top 10 Hiring Companies')
+
+df['seniority_level'].value_counts().plot(kind='pie', autopct='%1.1f%%', title='Seniority Level Distribution')
+
+df['job_title'].value_counts().head(15).plot(kind='bar', title='Top 15 Job Titles')
+
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, accuracy_score
+import warnings as w
+w.filterwarnings('ignore')
+
+# Load data (assuming the previous steps for loading and cleaning the data were successful)
+# df=pd.read_csv('//content/1000_ml_jobs_us.csv')
+# ... (previous data cleaning and preparation steps) ...
+
+le = LabelEncoder()
+
+# Apply LabelEncoder to all relevant categorical columns outside the training loop
+for col in ['job_posted_date', 'company_address_locality', 'company_address_region', 'company_name', 'job_title']:
+  df[col] = le.fit_transform(df[col].astype(str))
+
+# Define features (X) and target (y) after encoding
+X = df.drop('seniority_level', axis=1)
+y = le.fit_transform(df['seniority_level']) # Encode the target variable as well
+
+# Perform the train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initialize and train the model
+model = RandomForestClassifier(random_state=42)
+model.fit(X_train, y_train)
+
+# Make predictions and evaluate the model
+y_pred = model.predict(X_test)
+
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print(classification_report(y_test, y_pred))