You will get Machine Learning and Data Science solutions to your data-oriented problems
Project details
You will receive a Jupyter notebook with organized modular python code, where each line of code is explained in sufficient detail to understand all the steps of the process and the reasoning behind them.
This project covers:
• Data cleaning and preparation
• Imputing missing values
• Train-validation-test splits
• One-hot-encoding, data normalization, and scaling
• Fitting ML algorithms (linear regression, logistic regression, KNN, SVM, decision trees, random forest, etc...)
• Model selection using cross-validation/grid-search
• Generating predictions and evaluating the model on the test set
If needed, you can request custom evaluation metrics to be developed for your project that better reflect the costs associated with different types of classification errors.
This project covers:
• Data cleaning and preparation
• Imputing missing values
• Train-validation-test splits
• One-hot-encoding, data normalization, and scaling
• Fitting ML algorithms (linear regression, logistic regression, KNN, SVM, decision trees, random forest, etc...)
• Model selection using cross-validation/grid-search
• Generating predictions and evaluating the model on the test set
If needed, you can request custom evaluation metrics to be developed for your project that better reflect the costs associated with different types of classification errors.
Machine Learning Tools
Microsoft Excel, pandas, Python, scikit-learn, SQL, Stata, TableauWhat's included
| Service Tiers |
Starter
$100
|
Standard
$300
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 4 days | 4 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 5 | 5 | 6 |
Number of Scenarios | 1 | 2 | |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $500
Additional Revision
+$50
Additional Model Variation
+$60
Additional Scenario
(+ 1 Day)
+$200
Model Documentation
+$100Frequently asked questions
About Elyas
Data Scientist & Analyst | ML Modelling for Data-Driven Decisions
Helsinki, Finland - 5:36 pm local time
Projects I worked on:
1) Econometric Analysis Using National Survey Datasets:
-Examining data documentation/metadata to understand how variables are constructed and the contents of the questionnaires used to obtain the data
-using survey sample weights to ensure that the sample accurately reflects the characteristics of the population for both descriptive statistical analysis and inferential statistical analysis (e.g., regression)
- applying various econometric procedures such as: linear regression, logit/probit, multinomial logit/probit, ordered logit/probit, DID, RDD, 2SLS/IV, and panel data
2) Predictive Supervised Machine Learning for Binary Prediction Problems (Credit Scoring):
-Preprocessing data for ML algorithms (data cleaning and wrangling, imputing missing values, one-hot-encoding and data normalization, feature selection, and train-validation-test splits )
- Creating customized evaluation metrics that are tailored to the specific decision-making context
-Utilising survey sample weights when needed
3) Customer Segmentation Analysis (Unsupervised ML)
4) Generating Excel reports for marketing performance monitoring and analysis for a Japanese Corporation
I possess a broad range of data analytical skills, including:
- Data cleansing and wrangling
- Exploratory data analysis
- Data visualization
- Causal inferences
- Supervised ML modeling for regression and classification (taking into account the costs associated with different types of classification errors)
- Unsupervised ML and customer segmentation.
The tools I mostly work with are SQL, Python (NumPy, Pandas, Matplotlib, Seaborn, SciPy, Scikit-learn), Stata, and Excel.
Successful project completion is a priority for me, and I believe that clear communication is a critical factor in achieving this goal.
Steps for completing your project
After purchasing the project, send requirements so Elyas can start the project.
Delivery time starts when Elyas receives requirements from you.
Elyas works on your project following the steps below.
Revisions may occur after the delivery date.
Data Cleaning and Preparation
Clean the data and deal with missing values
Model Selection
Split the data into train-validation-test sets, and then select the best-performing model on the validation set using a specified evaluation metric