career-prediction-ai

Create a simple, clean, fully working **AI & Data Science Predictive Website** for my BCA college project. ## Project Title **AI-Based Career Prediction System** The main purpose of the website is to demonstrate the complete Data Science/AI workflow: **Data Collection → Data Cleaning → EDA → Visualization → Preprocessing → Feature Engineering → Machine Learning → NLP → Deep Learning → AI/LLM → Prediction** The final prediction should use the user's **Education, Skills and Experience** to predict a suitable career/job role. --- # WEBSITE PAGES ## 1. Home / Dashboard Show: * Project title * Short project description * Number of records * Number of features * Machine Learning model used * Model accuracy * Navigation to all modules Also show a simple workflow: **Data → Cleaning → Analysis → ML → AI → Prediction** --- ## 2. Data Collection Purpose: Demonstrate how data is collected. Features: * Upload CSV file * View uploaded dataset * Show number of rows and columns * Show column names * Show first 5/10 records * Allow downloading the dataset Use a sample career dataset containing: * Education * Specialization * Skills * Programming Level * Experience * Projects * Certifications * Career/Job Role --- ## 3. Data Cleaning Purpose: Demonstrate how raw/messy data is cleaned. Show: * Missing values * Duplicate records * Incorrect data types * Missing value handling * Duplicate removal * Cleaned dataset Display before/after statistics. Example: **Before Cleaning** * Rows: 500 * Missing values: 25 * Duplicates: 8 **After Cleaning** * Rows: 492 * Missing values: 0 * Duplicates: 0 --- ## 4. Exploratory Data Analysis (EDA) Purpose: Understand the dataset. Show: * Dataset statistics * Mean * Median * Minimum * Maximum * Standard deviation * Most common education * Most common skill * Most common career Also show useful insights from the dataset. Example: > Python is one of the most common skills among Data Analyst records. --- ## 5. Data Visualization Purpose: Represent data using graphs. Create interactive/simple charts such as: * Education distribution * Skills distribution * Career distribution * Experience distribution * Certification distribution * Education vs Career * Skills vs Career Use: * Matplotlib * Plotly Graphs should update based on the selected dataset. --- ## 6. Data Preprocessing Purpose: Prepare data for Machine Learning. Show: * Categorical encoding * Numerical feature processing * Missing value handling * Feature scaling where required * Train/Test split Explain briefly what preprocessing is doing. Example: **Education → One Hot Encoding** **Experience → Numerical Encoding** **Skills → Multi-label Encoding** --- ## 7. Feature Engineering Purpose: Create useful features from existing data. Create features such as: * Number of Skills * Number of Projects * Experience Score * Certification Score * Programming Skill Score Show the new features in a table. Example: **Python + SQL + ML = Skill Count 3** --- ## 8. Machine Learning Purpose: Train a real Machine Learning model. Use: * Random Forest * Logistic Regression Allow the user to select the model. Show: * Training dataset * Testing dataset * Accuracy * Precision * Recall * F1 Score * Confusion Matrix Display the trained model information. The ML model should actually be used for the final career prediction. --- ## 9. NLP Purpose: Demonstrate Natural Language Processing. Add a small text input: **"Enter your skills or career interest:"** Example: > I know Python, SQL and data visualization. Process the text using: * Text cleaning * Tokenization * TF-IDF Then identify relevant skills/career keywords. Show: **Detected Skills:** * Python * SQL * Data Visualization Also provide a simple sentiment analysis demonstration using sample feedback data. --- ## 10. Deep Learning / Neural Network Purpose: Demonstrate a basic neural network. Create a simple neural network using: * Scikit-learn MLPClassifier or TensorFlow/Keras if appropriate. Use the processed career dataset. Show: * Neural network architecture * Training accuracy * Testing accuracy * Loss/accuracy graph if available Keep this section simple because it is for a BCA academic project. --- ## 11. AI / LLM Purpose: Demonstrate Generative AI. Create a simple **AI Career Assistant**. The user can ask questions such as: > Which skills should I learn for Data Science? > What career can I choose after BCA? > How can I improve my Python skills? Use an LLM API through environment variables. If no API key is available, show a clear message and keep the rest of the website fully functional. Do NOT hardcode any API key. --- # 12. Career Prediction — MAIN PAGE This is the most important page. Create a simple form: ### Education * 10th * 12th * Diploma * BCA * B.Tech * MCA * Other ### Specialization * Computer Science * IT * Data Science * AI/ML * Software Engineering * Other ### Skills Allow multiple selections: * Python * Java * C/C++ * JavaScript * HTML/CSS * SQL * Machine Learning * Data Analysis * Data Visualization * AI * Communication * Problem Solving ### Experience * Fresher * <1 Year * 1–2 Years * 2–5 Years * 5+ Years ### Projects * 0 * 1 * 2–3 * 4+ ### Certification * Yes * No Then add a large button: **🔮 PREDICT CAREER** --- # 13. Prediction Result After prediction, show: ### Predicted Career Example: **Data Analyst** ### Confidence **85%** ### Recommended Skills * Python * SQL * Data Analysis * Power BI ### Why this prediction? Show simple explanations based on the user's input. Example: > Your prediction is influenced by your Python, SQL and Data Analysis skills and your BCA background. Also show the **top 3 possible career roles** with their prediction probabilities, without making the interface complicated. --- # 14. Project Report Create a page containing: * Introduction * Problem Statement * Objectives * Dataset * Data Collection * Data Cleaning * EDA * Visualization * Preprocessing * Feature Engineering * Machine Learning * NLP * Deep Learning * AI/LLM * Prediction * Results * Limitations * Future Scope * Conclusion Add a **Download Report** button. --- # TECHNOLOGY Use: * Python * Streamlit * Pandas * NumPy * Scikit-learn * Matplotlib / Plotly * NLP with TF-IDF * MLP Neural Network * LLM API Keep the project simple and understandable for a BCA student. --- # IMPORTANT REQUIREMENTS The website must be: * Fully working * Simple to understand * Clean and professional * Beginner-friendly * Responsive * No fake buttons * No placeholder pages * No hardcoded prediction results * Real dataset processing * Real Machine Learning prediction * All pages connected through navigation Create these files: ```text app.py requirements.txt README.md data/career_dataset.csv ``` The website should run using: ```bash pip install -r requirements.txt streamlit run app.py ``` Make sure the final project has **one clear purpose: use the complete Data Science/AI workflow to build a Career Prediction System**, while each page demonstrates one specific topic.