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1. Data 1-942 kosong/tidak lengkap, yaitu data pada kolom :
- NOMOR KLBI
- NAMA PRODUK
- TAHUN DATA

2. Terdapat beberapa data yang di highlight dengan tulisan merah, seperti planet soccer, kedai narsis, dan lain-lain

3. Terdapat missing value pada Data IKM dari Sentra Industri Kecil Somber

4. Data pada kolom TENAGA KERJA tidak balance dengan kasus data laki-laki hanya terisi dari tahun 2020 hingga 2021 dan sisanya missing value dan untuk data perempuan lebih dominan terisi datanya

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PIC : ALL STAFF
Working Method : Kanban Method

Result and Decision Making to Bussiness Case

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PIC : Faiq Azmi, Jumina Cahyani & Ragil Prasetyo
Working Method : Student Center Learn and Agile Method

Descriptive Modelling : is a mathematical process that describes real-world events and the relationships between factors responsible for them
Predictive Modelling :  the process of using known results to create, process, and validate a model that can be used to forecast future outcomes.
Traning Model & Evaluation : are systematic frameworks for investigating and analyzing the effectiveness of training or learning journeys.

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PIC : Athoillah Sholahuddin & Elsa Mardhiyah
Working Method : Student Center Learn and Agile Method

Data Cleansing :  the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled.
Exploratory Data Analysis (EDA) :  is the process of investigating the dataset to discover patterns, and anomalies (outliers), and form hypotheses based on our understanding of the dataset.
Feature Engineering : Feature engineering is a machine learning technique that leverages data to create new variables that aren’t in the training set. It can produce new features for both supervised and unsupervised learning, with the goal of simplifying and speeding up data transformations while also enhancing model accuracy.
Data Splitting : is a process used to separate a given dataset into at least two subsets called 'training' (or 'calibration') and 'test' (or 'prediction')

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PIC : ALL STAFF
Working Method : Kanban Method

Bussiness Understanding
Prepare : Dataset From Client, Analyst Dataset
Analyst Requirment : Syncrounous conference
Scenario : How to Understanding of problem and Steps for problem solve (Preprocessing problem, Method or Techinque, and Visualization Problem), Design Thinking of Problem, Guide for steps of rules
Report : Result From Scenario
Decisions :  rules for solution

Analytics Understanding
Data Wraling : The process of transforming raw data into a neater format. Prepare data during interactive data analysis and model building.
Statistical Impelementation : Application of statistics

Tools : Python. Library Python Numpy, Pandas, Matplotlib, Seaborn ,Statsmodels, Scipy, Scikit-learn

Data Manipulation : The process of manipulating or changing information to make it more organized and readable.

Machine Learning Technique : Introduce implementation of Machine Learning (Optional : if the request requires a solution method of Machine Learning)

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Implementation of Machine Learning Algorithm

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Implementation of Machine Learning Algorithm

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- Indexing, Slicing, dan Transforming
- Handling Missing Values
- Pivot, Melt, Stack & Unstack
- Aggregation & GroupBy
- Time Series

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Regresi Linear Method

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Data Visualization on Statistical Technique

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Data Visualization on Statistical Technique

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Measures of central tendency,  Measure of dispersion and
Correlation

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Numpy, Pandas, Matplotlib, Seaborn ,Statsmodels, Scipy, Scikit-learn

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Scope Task
1. Business Understanding &  Analytics Understanding : All Staff
2. Data Preprocessing : Athoillah Sholahuddin,  Elsa Mardiyah
3. Modelling & Implementation Model : Faiq Azmi, Jumina Cahyani, Ragil Prasetyo
4. Interpretation Result & Evaluation : All Staff