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Abhijeet Goel

[Part II] End to End Guide for heart disease prediction : Modelling

[Part II] End to End Guide for heart disease prediction : Modelling

Introduction After data collection and data preprocessing, we have to build the model, train it, validate it and test it. In this article, we will delve into Model creation, its architecture, training, validation and testing. The input all_features = layers.concatenate( [ sex_encoded, cp_encoded, fbs_encoded, restecg_encoded, exang_

Launching our Model into Production!!

[Part IV] End to End Guide for heart disease prediction : Deployment with Flask

Introduction Model deployment is crucial as it bridges the gap between theoretical models and real-world applications. It enables practical utilisation of machine learning and AI solutions, delivering valuable outcomes to businesses and users. Efficient deployment ensures scalability, reliability, and accessibility, maximising the impact of AI technology in various domains. We

[Part III] End to End Guide for Heart Disease Prediction : Tracking with MlFlow

[Part III] End to End Guide for Heart Disease Prediction : Tracking with MlFlow

Introduction MLflow is an open-source platform designed to manage the machine learning models. It enables data scientists and engineers to track experiments. With components like tracking, projects, and models, MLflow allows users to organise and reproduce experiments efficiently. It supports various machine learning frameworks and cloud platforms, promoting collaboration and

Model Evaluation Metrics

Model Evaluation Metrics

Evaluation Metrics are quantitative measures that are used to assess the performance of a statistical or machine-learning model. These metrics provide insights as to how well the model is performing and help in comparing different models and algorithms. There are two types of models, regression model (continuous output) and classification

Exploratory Data Analysis

Exploratory Data Analysis

In the previous article, we explored the different data preprocessing steps using the Auto MPG dataset. In this article, we will be looking at exploratory data analysis and the different methods. Exploratory Data Analysis is used to analyze and investigate datasets and summarize their main characteristics, often employing data visualization

Data Preprocessing: Enhancing Data Quality and Analysis Accuracy

Real-world data is often incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data, Noisy: containing errors or outliers. Inconsistent: containing discrepancies in codes or names. Data preprocessing is a technique that converts raw data into understandable data. It transforms the data into a format that

Tacotron-2 and WaveGlow model for Audio Deepfake generation

Tacotron-2 and WaveGlow model for Audio Deepfake generation

AI is used to gather data to create natural sounding voices that can read digital text. Hence audio deep fakes are closely related to text to speech - TTS technology. Text to speech (TTS) is a technology that converts text input into spoken audio. It can read aloud PDFs, websites,