Mini Project
Data Science
Personal Finance Analytics
Statement parsing, auto-categorisation and spend forecasting
- Python
- Pandas
- Prophet
- Streamlit
- +2
₹2,299₹3,999
4.5/198 reviews
Data Science · Major Project
Time Series Forecasting with LSTM
Multivariate LSTM forecasting of energy demand, benchmarked on ARIMA
3-4 weeks build
Overview
Forecasts household electricity demand from the UCI Individual Household Electric Power Consumption dataset using several input variables at once. The pipeline resamples and scales the series, builds sliding windows, and trains stacked LSTM models for one-step and multi-step horizons. Every model is compared against naive, ARIMA and SARIMA baselines using walk-forward validation.
What makes it stand up
- 01Resampling, gap filling and scaling for multivariate series
- 02Sliding-window dataset builder with configurable horizon
- 03Stacked and encoder-decoder LSTM architectures
- 04ARIMA and SARIMA baselines tuned with AIC
- 05Walk-forward validation with RMSE and MAE in the report
Module breakdown
- 01
Preparation
Cleans, resamples and scales the raw readings.
- 02
Windowing
Turns series into supervised input and target windows.
- 03
Deep models
Trains LSTM variants with early stopping.
- 04
Baselines
Fits naive, ARIMA and SARIMA models for comparison.
- 05
Forecast app
Streamlit view of forecasts against actual demand.
After this, you will be able to
- Explain how an LSTM cell keeps memory across time steps
- Defend walk-forward validation over a random split
- Show where the LSTM beats ARIMA and where it does not
Technology stack
- Python
- TensorFlow
- Pandas
- statsmodels
- Streamlit
- Level
- Major Project
- Domain
- Data Science
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support