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License: BSD 3-Clause "New" or "Revised" License
Code library for common machine learning algorithms
License: BSD 3-Clause "New" or "Revised" License
Time series module to predict the next n intervals.
Time series module for building forecasting models with/without exogenous variables using SARIMAX.
df: pandas.DataFrame
y_var: str
x_var: List[str]
params: Dict
model: object
model_summary: Dict
Expected API:
mod = AutoArima(df=df_ip,
y_var="y",
x_var=["cost", "stock_level", "retail_price"])
df_op = mod.predict(x_predict)
v0.4.0
Transportation problem.
Transportation problem using integer programming.
df: pandas.DataFrame
method: Union[str]
param: Dict
solve
No response
v0.4.0
Ensemble forecasting model.
Bates & Granger approach to combine forecasts to predict the next n intervals.
df: pandas.DataFrame
y: str
y_hat: List[str]
pred_period: int
lag: int
model: object
weights: Dict
Expected API:
mod = BatesGrager(df=df_ip,
y="y",
y_hat=["y_hat_01", "y_hat_02",
"y_hat_03", "y_hat_04"],
lag=53, pred_period=1)
df_op = mod.solve()
v0.4.1 (Default)
Implement Work force planning for House construction from attached PDF
Build a module to implement constraint programming using CP optimizer
Input data
Output data
No response
Generate workforce planning solution
v0.5.0 (Default)
Time series method to predict the next n intervals.
Add a time series method for building forecasting models with/without exogenous variables using GLMNet.
df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict
predict
No response
Integration tests
v0.4.0
GLMNet module.
GLMNet module for building models. Determine optimal alpha and lambda.
df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
param: Dict
fit
predict
No response
Integration tests
v0.4.0
General additive model framework to predict the next n intervals.
Add a GAM based time series method for building forecasting models.
df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict
predict
No response
Integration tests
v0.4.0
XGBoost module.
XGBoost module for both classification and regression. Determine optimal hyperparameters
Methods:
df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict
fit
predict
No response
v0.4.0
kNN module.
kNN module for both classification and regression. Determine optimal k.
Methods:
df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict
fit
predict
No response
v0.4.0
Time series method to predict the next n intervals.
Add a time series method for building forecasting models with/without exogenous variables using XGBoost.
df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict
predict
No response
Integration tests:
v0.4.0
Time series method to predict the next n intervals.
Add a time series method for building forecasting models with/without exogenous variables using Random forest.
df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict
predict
No response
Integration tests:
v0.4.0
Time series method to predict the next n intervals.
Add a time series method for building forecasting models with/without exogenous variables using fbprophet.
df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
param: Dict
predict
No response
v0.4.0
Traveling salesman problem.
Travelling salesman problem with both integer programming and heuristic.
Methods:
df: pandas.DataFrame
method: Union[str]
param: Dict
solve
v0.4.0
Random forest module.
Random forest module for both classification and regression. Determine optimal hyperparameters
Methods:
df: pandas.DataFrame
y_var: List[str]
x_var: List[str]
method: Union[str]
param: Dict
fit
predict
No response
v0.4.0
Update README for module changes and add projects view.
Incorrect labels.
v0.4.0
No response
Add a contributor code of conduct in the next available release.
We are missing a Code of conduct for the repository which is the only piece missing for the community profile.
v0.4.0
No response
A clustering module to cluster any given data (categorical/continuos/ordinal) and returns optimal clustering solution.
Compute optimal clustering solution using gap-statistic.
Methods:
df: pandas.DataFrame
x_var: List[str]
max_cluster: int
method: Union[str]
opt_k
No response
Integration tests:
v0.4.0
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