autopilotml
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# Autopilotml
> Automated machine learning library for analytics
## Installation
- `pip install autopilotml`
## Usage
### Load data
```python
from autopilotml import load_data, load_database
# For csv files
df = load_data(path = "dataset/titanic_train.csv", csv=True, **kwargs)
# For excel notebook
df = load_data(path = "dataset/titanic_train.xlsx", excel=True, **kwargs)
# To Load data from Database
# This framework supports sqlite, 'mysql', 'postgres', 'MongoDB'
df = load_database(database_type='sqlite', sqlite_db_path = 'database.db', query='select * from employee_table')
```
### Data Preprocessing
```python
from autopilotml import preprocessing
# If changing any values in the dictionary, whole dictionary has to be provided.
df = preprocessing(dataframe=df, label_column='Survived',
missing={
'type':'impute',
'drop_columns': False,
'threshold': 0.25,
'strategy_numerical': 'knn',
'strategy_categorical': 'most_frequent',
'fill_value': None},
outlier={
'method': 'None',
'zscore_threshold': 3,
'iqr_threshold': 1.5,
'Lc': 0.05,
'Uc': 0.95,
'cap': False})
```
### Data Transformation
```python
from autopilotml import transformation
# If the target_transform is true, then the function return 3 objects, (e.g) dataframe, feature encoder and target encoder
# else it will return 2 objects dataframe and feature encoder
df, encoder = transformation(dataframe=df,
label_column='Survived',
type = 'ordinal',
target_transform = False,
cardinality = True,
Cardinality_threshold = 0.3)
```
### Scaling
```python
# Here if target_scaling = True only applicable for regression then it will return 3 objects dataframe, feature scaler and target scaler
from autopilotml import scaling
df, scaler = scaling(df, label_column= 'Survived', type = 'standard', target_scaling = False)
```
### Feature Selecction
```python
from autopilotml import feature_selection
df, selector = feature_selection(dataframe=df, label_column='Survived',
estimator='RandomForestClassifier',
type='rfe', max_features=10,
min_features=2, scoring= 'accuracy',
cv=5)
```
### Model Training
```python
from autopilotml import training
model = training(dataframe=df, label_column='Survived', model_name='SVC', problem_type='Classification',
target_scaler=None, test_split =0.15, hypertune=True, n_epochs=100)
```
### MLFlow - Track the Model Training and model Parameters
```python
!mlflow ui
```
Raw data
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"description": "<div align=\"center\">\n <p>\n <a align=\"center\" href=\"\" target=\"_blank\">\n <img\n width=\"1280\"\n src=\"https://raw.githubusercontent.com/shyam1326/autopilotml/main/images/autopilotml.png\"\n </a>\n </p>\n\n\n[](https://badge.fury.io/py/autopilotml)\n<a href=\"https://pepy.tech/project/autopilotml\"><img src=\"https://pepy.tech/badge/autopilotml\" alt=\"total autopilotml downloads\"></a>\n[](LICENSE)\n[](https://colab.research.google.com/github/shyam1326/autopilotml/blob/main/autopilotml/research/autopilotml_examples.ipynb)\n\n\n</div>\n\n\n# Autopilotml\n> Automated machine learning library for analytics\n\n## Installation\n\n- `pip install autopilotml`\n\n## Usage\n\n### Load data\n\n```python\nfrom autopilotml import load_data, load_database\n\n# For csv files\ndf = load_data(path = \"dataset/titanic_train.csv\", csv=True, **kwargs)\n\n# For excel notebook\ndf = load_data(path = \"dataset/titanic_train.xlsx\", excel=True, **kwargs)\n\n# To Load data from Database\n\n# This framework supports sqlite, 'mysql', 'postgres', 'MongoDB'\ndf = load_database(database_type='sqlite', sqlite_db_path = 'database.db', query='select * from employee_table')\n```\n\n### Data Preprocessing\n\n```python\nfrom autopilotml import preprocessing\n\n# If changing any values in the dictionary, whole dictionary has to be provided.\n\ndf = preprocessing(dataframe=df, label_column='Survived',\n missing={\n 'type':'impute',\n 'drop_columns': False, \n 'threshold': 0.25, \n 'strategy_numerical': 'knn',\n 'strategy_categorical': 'most_frequent',\n 'fill_value': None},\n outlier={\n 'method': 'None',\n 'zscore_threshold': 3,\n 'iqr_threshold': 1.5,\n 'Lc': 0.05, \n 'Uc': 0.95,\n 'cap': False})\n```\n\n### Data Transformation\n\n```python\nfrom autopilotml import transformation\n\n# If the target_transform is true, then the function return 3 objects, (e.g) dataframe, feature encoder and target encoder\n# else it will return 2 objects dataframe and feature encoder\ndf, encoder = transformation(dataframe=df,\n label_column='Survived', \n type = 'ordinal',\n target_transform = False, \n cardinality = True, \n Cardinality_threshold = 0.3)\n```\n\n### Scaling\n\n```python\n# Here if target_scaling = True only applicable for regression then it will return 3 objects dataframe, feature scaler and target scaler\n\nfrom autopilotml import scaling\n\ndf, scaler = scaling(df, label_column= 'Survived', type = 'standard', target_scaling = False)\n```\n\n### Feature Selecction\n\n```python\nfrom autopilotml import feature_selection\n\ndf, selector = feature_selection(dataframe=df, label_column='Survived', \n estimator='RandomForestClassifier', \n type='rfe', max_features=10, \n min_features=2, scoring= 'accuracy', \n cv=5)\n```\n\n### Model Training\n\n```python\nfrom autopilotml import training\n\nmodel = training(dataframe=df, label_column='Survived', model_name='SVC', problem_type='Classification', \n target_scaler=None, test_split =0.15, hypertune=True, n_epochs=100)\n```\n\n### MLFlow - Track the Model Training and model Parameters\n\n```python\n!mlflow ui\n```\n",
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