Lineaarinen regressio Jobs

Proposal using CRISP-DM (not coding): •Describe problem, data, prep, modeling, evaluation •When doing Data analysis, add before and after measurement per company •Use this survey to extract the data structures and the data lookup: •Generate random dataset with 1000 records for the form shown at open-dma link above. Strictly from what the form offers!!! Generate Before and then a After dataset (a separate one with improved results). Save it to excel as and •Process the data in python •Create a function in python that will generate individual reports (word or PDF format) – sample 5 reports (for delivery), not 1000 and produce them as files. •Create visuals for the web report or individual reports as needed. •Elaborate any correlation between ...

Proposal using CRISP-DM (not coding): •Describe problem, data, prep, modeling, evaluation •When doing Data analysis, add before and after measurement per company •Use this survey to extract the data structures and the data lookup: •Generate random dataset with 1000 records for the form shown at open-dma link above. Strictly from what the form offers!!! Generate Before and then a After dataset (a separate one with improved results). Save it to excel as and •Process the data in python •Create a function in python that will generate individual reports (word or PDF format) – sample 5 reports (for delivery), not 1000 and produce them as files. •Create visuals for the web report or individual reports as needed. •Elaborate any correlation between ...

How do I make a script compatible and transferable from Tradingview to Webull Script Editor. It's called a Linear Regression Channel indicator for the chart. Here is the code: //@version=5 indicator('Linear Regression ++ [Dev Lucem]', 'Lin Reg ++ [DL]', overlay=true) mtf_val = ('', 'Resolution', inline="config", group="Algorithm Config") source = (close, inline="config", group="Algorithm Config") length = (100, minval=1, inline="config1", group="Algorithm Config") dev = input(2.0, 'Deviation', inline="config1", group="Algorithm Config") offset = (0, minval=0, inline="config2", group="Algorithm Config") smoothing = (1, minval=1, inl...

I need a partner who can walk me through the full process of quantifying and explaining feature importance across several classic models—specifically Linear Regression, Decision Trees and Support Vector Machines—using Python. The goal is to compare and contrast interpretability techniques such as SHAP, LIME, PDP and ICE, then package the findings so that non-technical stakeholders can easily understand why each feature matters. What I expect from you • Well-structured, reproducible Python code (preferably in Jupyter notebooks) showing how each model is trained and how the above interpretability methods are applied. • Clear visualisations and narratives that highlight where and why the different methods agree or diverge. • At least one live session (Zoom, M...