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Kyle van de Langemheen
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chore: add data path
1 parent 55507e0 commit 705cef0

2 files changed

Lines changed: 38 additions & 38 deletions

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‎notebooks/Lifelines_Modelling.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"# Lifelines Modelling\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "markdown",
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"id": "2",
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"metadata": {},
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"source": [
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"## Prepare the Input Data\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = su.read_sales_data()\n",
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"df = su.read_sales_data(\"../data/sales_df.csv\")\n",
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"len_b = len(df)\n",
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"df = df.drop_duplicates()\n",
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"len_a = len(df)\n",
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},
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{
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"cell_type": "markdown",
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"id": "4",
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"metadata": {},
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"source": [
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"## Build, Fit, and Rank with the Cox Model\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"source": [
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{
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"cell_type": "markdown",
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"id": "6",
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"metadata": {},
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"source": [
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"## Check Cox Assumptions\n",
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Evaluate Cox Performance\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9",
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"metadata": {},
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"source": [
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"cell_type": "markdown",
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"id": "10",
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"metadata": {},
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"source": [
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"## Compare Weibull and Log-Normal AFT Variants\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "11",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"We look at recall since we are only interested in a list of customers who will order in a certain intervall. We are not actually interested in the absolute order of the priority list. Recall tells us how many of the top k have been correctly included in the top k."
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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],
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"metadata": {
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"kernelspec": {
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"display_name": "sme-kt-zh-collaboration-forecasting",
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"display_name": "sme-kt-zh-collaboration-forecasting (3.12.3)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.14"
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"version": "3.12.3"
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}
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},
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"nbformat": 4,

‎notebooks/RSF_Modelling.ipynb‎

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"cells": [
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"id": "0",
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"metadata": {},
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"source": [
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"# RSF Modelling\n",
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{
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"cell_type": "code",
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"id": "1",
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"cell_type": "markdown",
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"## Prepare the Input Data\n",
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"df = su.read_sales_data()\n",
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"df = su.read_sales_data(\"../data/sales_df.csv\")\n",
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"len_b = len(df)\n",
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"df = df.drop_duplicates()\n",
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"len_a = len(df)\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"## Feature Engineering\n",
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{
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"## Train / Test Split and Survival Data Preparation\n",
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"## Fit the RSF Model\n",
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"## Evaluate RSF Performance\n",
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"## Tune Hyperparameters\n",
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"## Evaluate Tuned RSF and Compare\n",
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"## Results Discussion Optimized vs Unoptimized\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "sme-kt-zh-collaboration-forecasting",
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"display_name": "sme-kt-zh-collaboration-forecasting (3.12.3)",
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"language": "python",
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"name": "python3"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.14"
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"version": "3.12.3"
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}
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},
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"nbformat": 4,

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