Gaming Chatbot & Player Profiler

A compact Python toolkit for analysing gamers behavioural data and chatting with them through locally‑run Llama 2 models. The project demonstrates end‑to‑end ML: clustering, classification, sentiment analysis and an interactive recommendation bot. Using the K-means and RandomForest pipeline for optimal prediction capabilities.

Key Features

1. Data profiling — K-Means clustering (TrainProfiler.py) categorizes players into six intuitive segments.
2. Engagement prediction — Random-Forest classifier (RFTrainBot.py) forecasts each player's engagement level.
3. Three chatbots
   1. Heavy — fully automated ML + Llama; uses regex to extract user features (Chatbot_Heavy_Model.py).
   2. Light — semi-automated, keyword-triggered ML for user-feature extraction (Chatbot_Lighter_Model.py).
   3. Stupid — rule-based fallback with canned responses (Chatbot_Stupid.py).
4. Sentiment and keyword tracking with NLTK.
5. Local Llama 2 inference via llama-cpp-python (no external API keys required).

REQUIREMENTS

In order to run the light and heavy model, you must first install Visual Studio BuildTools for desktop. https://visualstudio.microsoft.com/visual-cpp-build-tools/ Under "Workloads" after installing the BuildTools Choose "Desktop Development with C++" and wait for it to install the packages.

Now you should be able to run it.

Customising the Models

Wish to try my models?

You can try editing the n_clusters in TrainProfiler.py. There's quite a few other settings to mess around with aswell. Watch out for the context window, the max is 4096, setting it higher results in crashing the program.