FrankRuns

I want a Python script demonstrating a simple approach for “shaking up” that historical data. Specifically, show me how to:

Load the Boston Housing dataset (or a similar publicly available dataset).
Split the data into training and test sets.
Add a small amount of random noise (jitter) to the training set features.
Train one linear regression model on the unmodified data and another on the jittered data.
Compare the MSE (Mean Squared Error) of each model on the same test set.
For the jitter, just use a normal distribution with a small standard deviation, something like 0.01. Then show me how the MSE differs between the original and jittered data. If the jittered version yields a lower MSE, let me know in the script output. If it’s worse, let me know that, too.

Nothing too fancy, just enough that I can make a point about how “bad data” might become surprisingly helpful when we own the uncertainty and inject it. And please include some print statements that display the MSEs. That’s it.

FrankRuns

You are a highly capable Python programmer who has access to locations.csv, which contains columns name, longitude, latitude, and type.

Please write a Python script that does the following:

Reads locations.csv into a pandas DataFrame.
Enumerates every possible Origin–Destination (OD) pair, but skips certain flows based on the following rules (via a helper function is_valid_flow(origin_type, dest_type)):
No shipments from Plant -> Customer
No shipments from DC -> Plant
No shipments from Customer -> DC
No shipments from Customer -> Plant

FrankRuns

FrankRuns

You have a CSV file called `locations.csv` with columns: name, longitude, latitude, type (including 'Customer' rows), DCs, and plants. 

I want you to:

1. Filter the data to only include rows where `type == 'Customer'`.

2. Generate synthetic one-period demand for these customers:

   - Normal scenario: Draw from a normal distribution (mean=100, std=20), clip negatives at 0.

FrankRuns

Generate a Python Script for [Project Objective] Visualization with [Visualization Tools] in a Jupyter Notebook

Body:
Objective:

Clearly describe the purpose of the project, the type of data involved, and the key insights or lessons you aim to convey through visualization. Mention whether you have an existing dataset or need to generate synthetic data.

Example:
Create a Python script to visualize supply chain network scenarios using Folium maps. The visualization should compare an optimal distribution strategy (multiple Distribution Centers) versus a suboptimal one (single Distribution Center) to highlight the impact on costs and delivery times. If no data file is provided, generate synthetic data for Distribution Centers (DCs) and Customers.

FrankRuns

Write a Python script to generate synthetic supply chain data with the following rules: 

Here is the detailed description based on the supply chain network:

Distribution Centers (DCs)
Washington, DC:
Located in the northeastern United States near major population centers.
Likely serves as a key hub for East Coast distribution.
Dallas, TX:
Positioned centrally in the southern United States.

FrankRuns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
from scipy.spatial.distance import cdist
import matplotlib.cm as cm

# Set random seed for reproducibility
np.random.seed(42)

FrankRuns

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import dowhy
from dowhy import CausalModel
import networkx as nx
import math
import sklearn 
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

FrankRuns

# Replicate Bob's results from this LinkedIn post:
# https://www.linkedin.com/posts/bob-wilson-77a22ab_people-sometimes-say-ab-testing-requires-activity-7152792859878871040-X1Sr?utm_source=share&utm_medium=member_desktop


### Implement Fisher's Exact Test

# Create the contingency table
contingency_table <- matrix(c(0, 4, 7, 3), nrow = 2)
dimnames(contingency_table) <- list(c("Control", "Treatment"),

FrankRuns

# Load Required Libraries
if (!require("MASS")) install.packages("MASS")
library(MASS)

# Define TV Shows
# A vector of TV show titles
tv_shows <- c(
  "Breaking Bad", "Game of Thrones", "The Wire", 
  "Stranger Things", "The Crown", "Mad Men", 
  "The Sopranos", "Friends", "The Office",