Predict Football Scores with Python & Machine Learning

Learn how to use Google Colab, a Jupyter notebook-based IDE hosted in the cloud, with free access to GPUs and TPUs for deep learning and no hardware setup required.

Import pandas, numpy, matplotlib, seaborn, and IPython.display to organize and visualize data; preprocess with IterativeImputer, StandardScaler, and LabelEncoder; build and evaluate Random Forest and XGBoost models using MSE and R2.

Download and extract the ESPN Soccer Data 2024 2025 from Kaggle to prepare it for predicting match scores, then set up Kaggle authentication in Google Colab and unzip the dataset.

Display the ESPN database schema diagram to understand the structure and relationships among leagues, teams, players, fixtures, standings, and key events for football match prediction.

Load the fixtures, team stats, standings, and leagues CSV files into memory with pandas read_csv, then use head to verify data readiness for exploration and model training.

Explore distributions across numerical data using the explore distributions function to compute mean, median, min, max, quartiles, and visualize with histograms and box plots to identify outliers and data issues.

Analyze fixture results by reviewing descriptive statistics columns, identify key features such as scores, attendance, and league IDs, and flag anomalies like negative venue IDs for clean data prep.

Analyze team stats to reveal how possession, discipline, positioning, shots, and defence influence match outcomes, guiding data preparation for a score prediction model.

Analyze the 2024 standings across leagues to reveal trends in rankings, games, and points. Examine goals for, goals against, goal difference, clean sheets, sanctions, and data completeness.

Analyze league distributions to reveal season type homogeneity and a constant year of 2024, while league IDs show wide dispersion with notable outliers and varied representation across leagues.

Validate standings data by checking for zero games and zero points, ensure required columns exist, and identify anomalies like disqualifications or penalties to clean data before analysis.

Inspect and validate teamstats.csv for inconsistencies by checking possession pct and pct-based metrics, ensuring nonnegative values, and confirming ratio consistency within a 10% tolerance to support reliable predictive analyses.

Conduct consistency check on leagues.csv to verify mandatory fields—league name, full league name, and league short name—detect missing entries and report anomalies to ensure a reliable dataset before prediction models.

Check fixtures.csv for inconsistencies by validating non-negative scores, ensuring event id, unique match id, venue id, home/away team ids align to teams and venues, dates 2010–2030; remove invalid venue data.

Merge and join fixtures, home and away team stats, standings, and leagues to create a single enriched data frame for training the prediction model.

Analyze missing values (NaN) in the merged data frame, categorize columns into four groups, and apply tailored strategies to improve data quality and reduce bias for predictive modeling.

Use bayesian linear regression for regression imputation to fill missing football match data, preserving variable relationships with iterative imputer and applying zero thresholds plus three-sigma limits.

Filter out matches with missing home or away rank, convert dates, and review home and away stats to ensure a reliable dataset for football predictive analytics.

Delete columns tied to future matches that contain over 92,000 NaN values to lighten the dataset and reduce noise, focusing the model on actual match predictions.

Remove non-essential columns to streamline analysis and score prediction, eliminating redundant league metadata and season type and ranking indicators.

Remove the away update time and home update time columns, which are timestamps of data updates. These columns track record changes but do not influence match score prediction.

Perform a final data integrity check on the merged dataset, confirming no missing values after cleaning and imputation, with complete home and away statistics across 152,147 rows and 107 columns.

Enrich your football data by creating synthetic indicators and derived variables, such as shot conversion rate, goal efficiency, and a weighted performance index, to improve predictive modeling.

Transform categorical variables into numerical features by label encoding season type, converting home/away winners to 1/0, and applying one hot encoding to status ID when cardinality is low.

Standardize numerical data with z-score normalization via scikit-learn's StandardScaler, centering at zero and scaling to unit variance; exclude event id and team ids, include stats like shots, fouls, and passes.

Analyze variable importance and feature selection for predicting home and away scores using two random forest regressors, averaging importances, and selecting top features via selectfrommodel.

Combine selected features into input data set x and predict home and away scores with a multi-output xgb regressor, using an 80/20 split and rmse evaluation.

Evaluate match score predictions using mean absolute error, median absolute error, and R2, reporting MAE 0.42 (home) and 0.37 (away) with R2 about 0.68 and 0.65.

Adapt and retrain the model on API subset features for a real-time web app, predicting home and away scores with a multi-output xgb regressor and RMSE evaluation.

Save the trained model with joblib as 'prediction of match results' and the scaler as 'scaler', ensuring the folder exists, then load them to predict new data with identical transformations.

Install PyCharm using the free community version to set up your development environment, download it for Windows, macOS, or Linux, and configure it to integrate and deploy your model.

Create an empty PyCharm project with an app.py Flask app, templates/index.html, a model folder for prediction of match results and train files, and a requirements file listing Flask, pandas, scikit-learn.

Access API football.com to retrieve match schedules, live scores, and team statistics for your prediction app, and get an API key with a plan that unlocks upcoming matches.

Integrate back-end logic with Flask to fetch upcoming fixtures and team stats from a football API, load a machine learning model and scaler, and expose routes for predicting match scores.

Integrate the front end with a Flask backend using index.html, CSS, and JavaScript to display a dynamic, user-friendly interface with real-time score predictions via /predict.

Launch the application by running app.py to access the web interface, view upcoming matches with dates and competitions, and use the 'predict the score' button for AI predictions.

Develop a real-time emotion recognition app from webcam video using Python and machine learning, displaying dominant emotions with confidence and providing downloadable code, model, and web interface.

Explore the Emotion Detection FIR dataset from Kaggle, featuring 35,685 black and white face images labeled for seven emotions, organized for training and testing and loadable with Keras or PyTorch.

Explore a 25-step ai model training pipeline in Google Colab, covering data preparation, augmentation, cnn architecture, training, evaluation, and saving models and graphs.

Analyzes the model’s test data performance, showing 67% overall accuracy with balanced macro and weighted averages, and highlights strong happy detection (F1 0.86, recall 85%), plus notable surprise (F1 0.8).

Explore the project structure in PyCharm and integrate a TensorFlow Keras emotion-detection model into a Flask app that streams facial emotion analysis from a webcam using mediapipe, OpenCV, and PIL.

Develop an ai that automatically detects and labels drones, airplanes, and helicopters in videos frame by frame, with annotation, a clean interface, and confidence scores trained on a Kaggle dataset.

Explore the drone detection dataset from Kaggle, featuring bounding boxes, coordinates, and YOLO-formatted annotations for drones, airplanes, and helicopters to train versatile object-detection models.

Explore an end-to-end training pipeline for aircraft detection with YOLOv8 in Google Colab, covering library setup, data preparation, annotation extraction, model training, and evaluation across 27 steps.

Analyze a Yolo V8 drone detector by tracking accuracy, recall, and map at 50% to 95% across 30 epochs to demonstrate effective detection and precise localization.

Analyze how the loss components decrease across 30 training epochs, improving box localization and classification, while validation losses trend down with stable generalization and no significant overfitting.

Integrate a pre-trained Yolo V8 aircraft detection model into a Flask web app, enabling video upload, frame-by-frame analysis with bounding boxes, labels, and confidence scores, and display of annotated results.

Develop an artificial intelligence system that detects road vehicles in video with yolo, frame-by-frame analysis, colored bounding boxes, confidence scores, and a scratch-trained model recognizing more than 20 vehicle categories.

Explore the road vehicle images dataset from Kaggle, designed for training YOLO models with real traffic images, bounding boxes, YAML class lists, and train/validation splits for autonomous vehicle contexts.

Learn to train an ai model end-to-end with the YOLOv9 pipeline in Google Colab, from data preparation and annotations to training and evaluating with precision, recall, and map.

Analyze the training results of the Yolo v9 model to see how it learns object detection, tracing box loss, class loss, DFL, and mean average precision toward validation data.

Integrate the YOLO v9 model into a Flask web app that uploads videos, processes frames with OpenCV to detect cars, motorcycles, trucks, and display annotated video.

Fine-tune the Marion MT model for technical English to French translation and deploy a web-based app with a browser interface, input field, translate button, and green translation display.

Fine-tune a pre-trained translation model, Marion MT, to translate English to French for technical text, retraining on 67,000 sentence pairs to adapt to software instructions, system messages, and parameter strings.

Discover how the KDE four dataset from KDE translations on Huggingface enables fine-tuning the Marion MT model for English to French in software interfaces, yielding fluent, terminologically consistent results.

learn to fine-tune a MarianMT english-french translation model in google colab, covering data preparation, deduplication, smart lowercase, tokenization, balancing, training with Hugging Face trainer, and saving to Google Drive.

Analyze final performance of the Marion, MT translation model fine-tuned on an English-French corpus, reporting stable loss, fast inference, and strong bleu, meteor, and igf quality metrics.

Explore how a Flask web app loads a Marian MT model and tokenizer to translate English to French, with a web interface and JSON API.

Develop a multilingual text summarizer web app in Python and Flask that supports English and French, using a fine-tuned Emballage 50 model for supervised summarization.

Explore how the facebook/mbart-large-50 model uses a bidirectional encoder and autoregressive decoder for multilingual summarization, pre-trained on 50 languages and fine-tuned with text-summary pairs.

Discover two essential datasets for fine tuning a multilingual summarization model: Xsum for English and ML sum for French, featuring BBC articles and journalistic abstracts.

Fine-tune the mBART 50 model on a bilingual English-French corpus to generate concise summaries. Train on XSum and Milsom data, then evaluate with rouge metrics.

Analyze the evaluation results of our text summary model, noting a loss of 1.69 and ROUGE-1: 33%, ROUGE-2: 14%, ROUGE-L: 26%. Improve speed and quality with optimization and GPU deployment.

Deploys a fine-tuned M Bart 50 multilingual summarization model for English and French within a Flask web app, handling language selection, GPU acceleration, and an animated processing spinner.

Present the final project showcasing an AI application that detects pneumonia from chest x-rays using a fine-tuned efficientnet B0, with a flask backend and bootstrap frontend for rapid pre-screening.

Leverages EfficientNetB0, a Google CNN, using compound scaling to balance depth, width, and input resolution for fast, accurate pneumonia detection on chest X-rays.

Explore the chest x-ray pneumonia dataset used to train a pneumonia detection model, with normal and pneumonia images organized into train, val, and test folders for binary classification.

Fine-tune EfficientNet B0 on chest x-ray pneumonia data using data augmentation, with pretrained ImageNet weights for binary classification, normal vs pneumonia, and evaluate via confusion matrix and classification report.

Analyze learning curves and test results to assess model learning, generalization, and regularization. Confirm high accuracy and near-perfect F1 (0.997) across both classes.

Define app.py routes to handle image uploads, run an efficientnet b0 model from an h5 file to diagnose pneumonia, and serve the ui via templates and a static upload folder.

Explore how the app.py file powers a pneumonia detection web app by connecting a pre-trained EfficientNet model to a Flask interface, processing uploaded x-rays, and returning diagnosis and confidence.

Analyze index.html to build bootstrap interface with a form that uploads an x ray image, triggers a flask backend, and shows pneumonia or normal with a confidence score and spinner.

Explore how artificial intelligence, machine learning, and deep learning enable human-like reasoning, speech recognition, and automated decision making, through neural networks and rule-based approaches.

Explore how artificial intelligence works, covering machine learning and deep learning, supervised and unsupervised learning, labeled data, datasets, features, and algorithms, plus the no free lunch principle guiding model choice.

Learn how supervised learning uses labeled data and feature relationships to predict outcomes, from travel time to apartment prices, by training on real data and refining with more data.

Explore supervised learning, including classification and regression, and key algorithms such as linear, logistic, polynomial, and ridge regression, while noting challenges from labeled data requirements and overfitting.

Explore unsupervised learning, where models discover patterns from unlabeled data using clustering, association, and autoencoders, while navigating limitations like imprecision and data quality.

Explore how deep learning uses artificial neural networks to learn from raw data. See how input, hidden, and output layers, weights, and activation functions shape predictions.

Learn how loss quantifies the difference between a neural network's prediction and the true label, and how backpropagation adjusts weights across layers to improve future predictions.

Activation functions transform neuron outputs into nonlinear values, enabling learning of complex relationships. The lecture covers sigmoid, tangent function, ReLU, leaky ReLU, and softmax, and notes vanishing gradients.

Artificial neural networks unleash vast potential across facial recognition, fraud detection, outcome prediction, weather forecasts, and medical diagnoses, and power speech recognition and natural language processing.

Compare cpu and gpu roles in training deep learning models for predicting football scores, highlighting backpropagation, parallel processing, large data handling, and support for TensorFlow, PyTorch, and Keras.

Explore tensor processing units (TPUs) and how Google-designed TPUs accelerate training and inference for deep learning models with TensorFlow by optimizing tensor calculations.

Explore TensorFlow, PyTorch, and Keras, three leading deep learning frameworks that leverage GPUs and TPUs to train models from simple neural networks to complex architectures.

Explore integrated development environments for deep learning, including Jupyter Notebook and Google Colab, to write, test, and debug models efficiently with GPU/TPU acceleration and real-time visualization.