Mango Tech Solutions
Data Science
DATA COLLECTION
The procedure starts with the investigation of business and market-oriented information and finds predictable links and patterns between factors. The investigated information is then gathered from different sources.
EXPLORATORY DATA ANALYSIS
This stage includes the analysis of information to boil down the primary qualities often with visual models. In light of these upgraded models, arrangements are created to meet your business necessities.
DATA PRE-PROCESSING
After the collection of data, this information is then pre-processed for the investigation. This incorporates normalization, data cleaning, dealing with missing qualities and so on. Data cleaning includes the elimination of incomplete.
FEATURE ENGINEERING
Feature Engineering includes the utilization of domain knowledge of information to create features that help in the operation of machine learning calculations. Along these lines, vital features for model building are planned, using different factor selection strategies.
MODEL BUILDING
Model building is the technique that creates, tests and approves the model when it comes to predicting the possibility of any results. Many machine learning calculations are attempted and their work evaluated to assemble predictive models and tailored algorithms for your business.
SOLUTION DEVELOPMENT
The last phase is of solution development where factor dispersion and connections among factors is analyzed. The model then is created by utilizing improved machine learning calculations continuously until ultimate accuracy is accomplished.
