Building Your ML Portfolio

Project 1: Classic ML (tabular data)
  → Shows core skills: EDA, feature engineering, model selection
  → Example: Churn prediction, fraud detection, price forecasting

Project 2: Deep Learning (images or text)
  → Shows neural network skills and transfer learning
  → Example: Custom image classifier, sentiment analyzer, NER system

Project 3: Something Novel (your domain or interest)
  → Shows initiative and problem-solving
  → Example: ML for your current job's data, Kaggle competition result,
             open-source contribution

# BAD: "I trained a Random Forest on the Titanic dataset"
(Everyone has done this; no one cares)

# GOOD: "Building a flight delay predictor for American airports:
         Can we predict >30-minute delays 2 hours before departure
         with 85%+ precision to enable rebooking before passengers
         arrive at the gate?"
(Real problem, specific success criterion, practical impact)

Dataset acquisition: Where it came from, any collection challenges
EDA: What you found, what surprised you, charts
Preprocessing: What cleaning was needed and why
Modeling: What you tried, what failed, why you chose the final approach
Evaluation: Honest assessment including failure modes
Deployment: Even a simple demo or API endpoint
Limitations: What doesn't this model handle well?

# app.py — minimal Streamlit app for your ML project
import streamlit as st
import pandas as pd
import pickle
import numpy as np

st.title("House Price Predictor")
st.write("Built with Gradient Boosting on Ames Housing Dataset")

# Input fields
col1, col2 = st.columns(2)
with col1:
    sq_ft = st.slider("Living Area (sqft)", 500, 5000, 1500)
    quality = st.slider("Overall Quality (1-10)", 1, 10, 6)
    
with col2:
    year_built = st.number_input("Year Built", 1900, 2024, 2000)
    garage_cars = st.selectbox("Garage Spaces", [0, 1, 2, 3])

if st.button("Predict Price"):
    # Load model and predict
    with open('model.pkl', 'rb') as f:
        model = pickle.load(f)
    
    features = pd.DataFrame([{
        'Gr Liv Area': sq_ft, 'Overall Qual': quality,
        'Year Built': year_built, 'Garage Cars': garage_cars,
        'House_Age': 2024 - year_built
    }])
    
    pred_log = model.predict(features)[0]
    pred = np.expm1(pred_log)
    
    st.metric("Estimated Price", f"${pred:,.0f}")
    st.write(f"±${pred * 0.10:,.0f} typical error (10%)")

# Run: streamlit run app.py

my-house-price-predictor/
├── README.md          ← Most important file
├── notebooks/
│   ├── 01_eda.ipynb
│   ├── 02_preprocessing.ipynb
│   ├── 03_modeling.ipynb
│   └── 04_evaluation.ipynb
├── src/
│   ├── preprocess.py
│   ├── train.py
│   └── predict.py
├── app.py             ← Streamlit demo
├── data/
│   └── README.md      ← Instructions to get the data (don't commit it)
├── models/
│   └── README.md      ← Model card with performance metrics
├── requirements.txt
└── .gitignore

# House Price Predictor

> Predicting residential sale prices in Ames, Iowa with ~8% median error.

## Results
| Model | CV RMSE | Test R² | Test MAE |
|-------|---------|---------|----------|
| Baseline (median) | $68K | — | $68K |
| Ridge Regression | $24K | 0.84 | $17K |
| **Gradient Boosting** | **$18K** | **0.91** | **$13K** |

**Live demo:** [streamlit-app-link]

## Problem
Given 80 features about a house (location, size, quality, age), 
predict its sale price. Target: beat median-price baseline by >50%.

## Key Findings
- Overall quality (1-10 rating) is the strongest predictor (26% importance)
- Living area × quality interaction explains premium homes
- Neighborhood captures school district + location value

## Limitations
- Trained on 2006-2010 data — may not reflect 2024 prices
- No geographic generalization (Ames, IA only)
- Unusual properties (historic homes, very large estates) have higher errors

# Option 1: FastAPI (for APIs)
from fastapi import FastAPI
from pydantic import BaseModel
import pickle, numpy as np

app = FastAPI()

class HouseFeatures(BaseModel):
    living_area: float
    overall_qual: int
    year_built: int

@app.post("/predict")
def predict(features: HouseFeatures):
    with open('model.pkl', 'rb') as f:
        model = pickle.load(f)
    
    X = [[features.living_area, features.overall_qual, 
          2024 - features.year_built]]
    pred = np.expm1(model.predict(X)[0])
    return {"predicted_price": round(pred, 2)}

# Deploy free: Railway, Render, Fly.io

Days 1-30: ML Fundamentals project
  Week 1-2: Find dataset, do EDA, baseline model
  Week 3: Feature engineering, model comparison
  Week 4: Polish, write README, build Streamlit demo

Days 31-60: Deep Learning project
  Week 1-2: Dataset preparation, architecture design
  Week 3: Training, evaluation, error analysis
  Week 4: Deploy on Hugging Face Spaces

Days 61-90: Novel project (your own idea)
  Week 1-2: Problem framing, data acquisition
  Week 3: Build and evaluate
  Week 4: Full writeup, blog post, LinkedIn post