diff --git a/assignment_4.ipynb b/assignment_4.ipynb
deleted file mode 100644
index 82a7329..0000000
--- a/assignment_4.ipynb
+++ /dev/null
@@ -1,80 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ff443e6e",
-   "metadata": {},
-   "source": [
-    "# Assignment 4\n",
-    "### Do three of four."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47949236",
-   "metadata": {},
-   "source": [
-    "### Exercise 1: Contingent Comparisons\n",
-    "- Load the Minnesota use of force data.\n",
-    "- Bootstrap the proportion of missing values for `subject_injury` for each race, and plot the results with grouped KDE and ECDF plots\n",
-    "- Describe what you see. When we consider second order uncertainty, how similar or different are the sampling distributions of these proportions? "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18fdd9ba",
-   "metadata": {},
-   "source": [
-    "### Exercise 2: Invitation to Inference\n",
-    "- Run the simulation code line by line and comment what each line is doing, or write your own code to do the resampling\n",
-    "- Open the NHANES or Ames prices or College Completion data. Pick a variable and a statistic to compute (e.g. mean, median, variance, IQR)\n",
-    "- Use the `simulate` function from class to get a sample of estimates for your statistic and your data\n",
-    "- Create a new function, `interval(L,H,estimates)`, that computes the $L$-th and $H$-th quantiles for your estimates, $H>L$\n",
-    "- If $L=.05$ and $H=.95$, this is a **90-percent confidence interval**: \"For our statistic, this interval captures the true value of the population parameter 90 percent of the time. (We are 90% **confident** that it includes the true value of the parameter, but the probability that the true parameter lies in this interval is 0 or 1.)\"\n",
-    "- We will spend much more time on this later in class, but for people who have done hypothesis testing before, you now know how to do it directly from the data: No central limit theorem required."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bab3ea0",
-   "metadata": {},
-   "source": [
-    "### Exercise 3: Intro to A/B Testing\n",
-    "- Go here, and read about this study: https://www.clinicaltrials.gov/study/NCT01985360\n",
-    "- Read the Study Overview and explain what the goal of the trial is \n",
-    "- Read the Study Plan and explain how it was designed and why -- there's lots of medical jargon, but the main point is how patients were assigned to interventions. \n",
-    "- Read the Results Posted: Go to **Outcome Measures**. Explain how table 1 (\"Incidence of Death from Any Cause or Myocardial Infarction\") is a contingency table. These are the data for this exercise.\n",
-    "- What is the difference in surival rates between the invasive strategy and the conservative strategy?\n",
-    "- Bootstrap the survival rates for the two groups, and plot them as KDEs and ECDFs against one another\n",
-    "- Bootstrap the difference in surival rates, and plot it as a KDE and ECDF\n",
-    "- Is this an effective health intervention? Explain your answer clearly\n",
-    "\n",
-    "This would be what CS people call **A/B testing** and everyone else called a **randomized controlled trial**: Using randomized assignment to detect the difference in outcomes between two groups. (We've just done a non-parametric version of a two-sample t-test.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbc5b568",
-   "metadata": {},
-   "source": [
-    "### Exercise 4: Prediction Uncertainty\n",
-    "- Pick a dataset and two continuous variables.\n",
-    "- Recall the LCLS estimator:\n",
-    "$$\n",
-    "\\hat{y}(z) =  \\frac{ \\frac{1}{N} \\sum_{i=1}^N y_i \\times \\frac{1}{h}k\\left( \\frac{z - x_i}{h} \\right)}{ \\frac{1}{N} \\sum_{i=1}^N \\frac{1}{h} k\\left( \\frac{z - x_i}{h} \\right)}\n",
-    "$$\n",
-    "with the Epanechnikov kernel and the standard plug-in bandwidth for $h$\n",
-    "- Compute and plot this line for 30 bootstrap samples. Notice where there is a lot of variation in the predictions, versus little variation in the predictions.\n",
-    "- Now, for any $z$, we can bootstrap a distribution of predictions using the above formula. Do this at the 25th percentile, median, and 75th percentile of $X$, and make KDE plots of your results.\n",
-    "- Now, pick a grid for $z$: Obvious choices are all of the unique values in the data, or an equally spaced grid from the minimum value to the maximum value. For each $z$, bootstrap a sample of predictions and compute the .05 and .95 quantiles. Plot these error curves along with your LCLS estimate. Where are your predictions \"tight\"/reliable? Where are they highly variable/unreliable?"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/assignment_4_exercise_4.ipynb b/assignment_4_exercise_4.ipynb
new file mode 100644
index 0000000..04276ba
--- /dev/null
+++ b/assignment_4_exercise_4.ipynb
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ff443e6e",
+   "metadata": {},
+   "source": [
+    "# Assignment 4\n",
+    "### Do three of four."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47949236",
+   "metadata": {},
+   "source": [
+    "### Exercise 1: Contingent Comparisons\n",
+    "- Load the Minnesota use of force data.\n",
+    "- Bootstrap the proportion of missing values for `subject_injury` for each race, and plot the results with grouped KDE and ECDF plots\n",
+    "- Describe what you see. When we consider second order uncertainty, how similar or different are the sampling distributions of these proportions? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "84823c24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18fdd9ba",
+   "metadata": {},
+   "source": [
+    "### Exercise 2: Invitation to Inference\n",
+    "- Run the simulation code line by line and comment what each line is doing, or write your own code to do the resampling\n",
+    "- Open the NHANES or Ames prices or College Completion data. Pick a variable and a statistic to compute (e.g. mean, median, variance, IQR)\n",
+    "- Use the `simulate` function from class to get a sample of estimates for your statistic and your data\n",
+    "- Create a new function, `interval(L,H,estimates)`, that computes the $L$-th and $H$-th quantiles for your estimates, $H>L$\n",
+    "- If $L=.05$ and $H=.95$, this is a **90-percent confidence interval**: \"For our statistic, this interval captures the true value of the population parameter 90 percent of the time. (We are 90% **confident** that it includes the true value of the parameter, but the probability that the true parameter lies in this interval is 0 or 1.)\"\n",
+    "- We will spend much more time on this later in class, but for people who have done hypothesis testing before, you now know how to do it directly from the data: No central limit theorem required."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bab3ea0",
+   "metadata": {},
+   "source": [
+    "### Exercise 3: Intro to A/B Testing\n",
+    "- Go here, and read about this study: https://www.clinicaltrials.gov/study/NCT01985360\n",
+    "- Read the Study Overview and explain what the goal of the trial is \n",
+    "- Read the Study Plan and explain how it was designed and why -- there's lots of medical jargon, but the main point is how patients were assigned to interventions. \n",
+    "- Read the Results Posted: Go to **Outcome Measures**. Explain how table 1 (\"Incidence of Death from Any Cause or Myocardial Infarction\") is a contingency table. These are the data for this exercise.\n",
+    "- What is the difference in surival rates between the invasive strategy and the conservative strategy?\n",
+    "- Bootstrap the survival rates for the two groups, and plot them as KDEs and ECDFs against one another\n",
+    "- Bootstrap the difference in surival rates, and plot it as a KDE and ECDF\n",
+    "- Is this an effective health intervention? Explain your answer clearly\n",
+    "\n",
+    "This would be what CS people call **A/B testing** and everyone else called a **randomized controlled trial**: Using randomized assignment to detect the difference in outcomes between two groups. (We've just done a non-parametric version of a two-sample t-test.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbc5b568",
+   "metadata": {},
+   "source": [
+    "### Exercise 4: Prediction Uncertainty\n",
+    "- Pick a dataset and two continuous variables.\n",
+    "- Recall the LCLS estimator:\n",
+    "$$\n",
+    "\\hat{y}(z) =  \\frac{ \\frac{1}{N} \\sum_{i=1}^N y_i \\times \\frac{1}{h}k\\left( \\frac{z - x_i}{h} \\right)}{ \\frac{1}{N} \\sum_{i=1}^N \\frac{1}{h} k\\left( \\frac{z - x_i}{h} \\right)}\n",
+    "$$\n",
+    "with the Epanechnikov kernel and the standard plug-in bandwidth for $h$\n",
+    "- Compute and plot this line for 30 bootstrap samples. Notice where there is a lot of variation in the predictions, versus little variation in the predictions.\n",
+    "- Now, for any $z$, we can bootstrap a distribution of predictions using the above formula. Do this at the 25th percentile, median, and 75th percentile of $X$, and make KDE plots of your results.\n",
+    "- Now, pick a grid for $z$: Obvious choices are all of the unique values in the data, or an equally spaced grid from the minimum value to the maximum value. For each $z$, bootstrap a sample of predictions and compute the .05 and .95 quantiles. Plot these error curves along with your LCLS estimate. Where are your predictions \"tight\"/reliable? Where are they highly variable/unreliable?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "52b44377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ames = pd.read_csv(\"data/ames_prices.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2cc6b289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import gaussian_kde\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "class LCLSEstimator:\n",
+    "    \"\"\"\n",
+    "    Implements the Local Constant Least Squares (Nadaraya-Watson) estimator.\n",
+    "    Uses the Epanechnikov kernel and plug-in bandwidth if not specified.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, x, y, h=None):\n",
+    "        self.x_name = x.name\n",
+    "        self.y_name = y.name\n",
+    "\n",
+    "        df = pd.DataFrame({'x': x, 'y': y}).dropna()\n",
+    "        self.x = df['x']\n",
+    "        self.y = df['y']\n",
+    "\n",
+    "        self.n = len(self.x)\n",
+    "        self.grid = np.linspace(self.x.min(), self.x.max(), 500)\n",
+    "        self.y_hat = None\n",
+    "\n",
+    "        if h is None:\n",
+    "            iqr = np.quantile(self.x, 0.75) - np.quantile(self.x, 0.25)\n",
+    "            self.h = 0.9 * min(np.std(self.x), iqr / 1.34) * self.n ** (-1/5)\n",
+    "            #print(f'Computed bandwidth (Silverman\\'s rule): {self.h}')\n",
+    "        else:\n",
+    "            self.h = h\n",
+    "            #print(f'Using provided bandwidth: {self.h}')\n",
+    "\n",
+    "    def epanechnikov_kernel(self, u):\n",
+    "        return 0.75 * (1 - u**2) * (np.abs(u) <= 1)\n",
+    "\n",
+    "    def fit(self):\n",
+    "        \"\"\"\n",
+    "        Computes the LCLS estimator using the Epanechnikov kernel.\n",
+    "        \"\"\"\n",
+    "        u = (self.grid.reshape(1, -1) - self.x.to_numpy().reshape(-1, 1)) / self.h\n",
+    "        K = self.epanechnikov_kernel(u)\n",
+    "\n",
+    "        numerator = (self.y.to_numpy().reshape(-1, 1) * K).sum(axis=0)\n",
+    "        denominator = K.sum(axis=0)\n",
+    "\n",
+    "        with np.errstate(divide='ignore', invalid='ignore'):\n",
+    "            self.y_hat = np.where(denominator != 0, numerator / denominator, np.nan)\n",
+    "\n",
+    "        return self.y_hat, self.grid\n",
+    "\n",
+    "    def plot(self, label=None, ci=None):\n",
+    "        \"\"\"\n",
+    "        Plot data and fitted curve (optionally with confidence interval).\n",
+    "        \"\"\"\n",
+    "        plt.figure(figsize=(6, 6))\n",
+    "        sns.scatterplot(x=self.x, y=self.y, alpha=0.1, label='Data')\n",
+    "        sns.lineplot(x=self.grid, y=self.y_hat, color='red',\n",
+    "                     label=label or f'LCLS (h={self.h:.2f})')\n",
+    "\n",
+    "        if ci is not None:\n",
+    "            lower, upper = ci\n",
+    "            plt.fill_between(self.grid, lower, upper, color='red', alpha=0.2, label='90% CI')\n",
+    "\n",
+    "        plt.xlabel(self.x_name.capitalize())\n",
+    "        plt.ylabel(self.y_name.capitalize())\n",
+    "        plt.title('Nadaraya-Watson with Epanechnikov Kernel')\n",
+    "        plt.legend()\n",
+    "        plt.grid(True)\n",
+    "        plt.show()\n",
+    "\n",
+    "    def bootstrap(self, n_bootstrap=30, seed=42, show_curves=True):\n",
+    "        \"\"\"\n",
+    "        Perform bootstrap to estimate prediction uncertainty.\n",
+    "        - Returns list of y_hat curves.\n",
+    "        \"\"\"\n",
+    "        np.random.seed(seed)\n",
+    "        bootstrap_curves = []\n",
+    "\n",
+    "        for _ in range(n_bootstrap):\n",
+    "            idx = np.random.choice(self.n, self.n, replace=True)\n",
+    "            x_sample = self.x.iloc[idx].reset_index(drop=True)\n",
+    "            y_sample = self.y.iloc[idx].reset_index(drop=True)\n",
+    "\n",
+    "            model = LCLSEstimator(x_sample, y_sample, h=self.h)\n",
+    "            y_hat_sample, _ = model.fit()\n",
+    "            bootstrap_curves.append(y_hat_sample)\n",
+    "\n",
+    "            if show_curves:\n",
+    "                plt.plot(self.grid, y_hat_sample, color='gray', alpha=0.3)\n",
+    "\n",
+    "        if show_curves:\n",
+    "            plt.plot(self.grid, self.y_hat, color='red', label='Original Fit', linewidth=2)\n",
+    "            plt.xlabel(self.x_name.capitalize())\n",
+    "            plt.ylabel(self.y_name.capitalize())\n",
+    "            plt.title('Bootstrap LCLS Estimates')\n",
+    "            plt.grid(True)\n",
+    "            plt.show()\n",
+    "\n",
+    "        return np.array(bootstrap_curves)\n",
+    "\n",
+    "    def kde_at_points(self, bootstrap_curves, quantiles=[0.25, 0.5, 0.75]):\n",
+    "        \"\"\"\n",
+    "        For selected quantiles of X, plot KDE of bootstrapped predictions.\n",
+    "        \"\"\"\n",
+    "        quantile_vals = np.quantile(self.x, quantiles)\n",
+    "        indices = [np.abs(self.grid - q).argmin() for q in quantile_vals]\n",
+    "\n",
+    "        for i, q in zip(indices, quantiles):\n",
+    "            samples = bootstrap_curves[:, i]\n",
+    "            kde = gaussian_kde(samples)\n",
+    "            x_vals = np.linspace(min(samples), max(samples), 200)\n",
+    "            plt.plot(x_vals, kde(x_vals), label=f'{int(q*100)}th percentile (x ≈ {self.grid[i]:.2f})')\n",
+    "\n",
+    "        plt.title('KDE of Bootstrap Predictions at Selected X Quantiles')\n",
+    "        plt.xlabel('Predicted Value')\n",
+    "        plt.ylabel('Density')\n",
+    "        plt.legend()\n",
+    "        plt.grid(True)\n",
+    "        plt.show()\n",
+    "\n",
+    "    def plot_with_confidence_band(self, bootstrap_curves):\n",
+    "        \"\"\"\n",
+    "        Plot the original fit along with 90% CI (5th and 95th percentiles from bootstrap).\n",
+    "        \"\"\"\n",
+    "        lower = np.percentile(bootstrap_curves, 5, axis=0)\n",
+    "        upper = np.percentile(bootstrap_curves, 95, axis=0)\n",
+    "        self.plot(ci=(lower, upper))\n",
+    "\n",
+    "\n",
+    "# === USAGE ===\n",
+    "\n",
+    "# Assume df2 is already defined\n",
+    "x = df_ames['area']\n",
+    "y = df_ames['price']\n",
+    "\n",
+    "# Instantiate model\n",
+    "model = LCLSEstimator(x, y)\n",
+    "model.fit()\n",
+    "\n",
+    "# Bootstrap\n",
+    "bootstrap_curves = model.bootstrap(n_bootstrap=30)\n",
+    "\n",
+    "# KDE at 25%, 50%, 75% quantiles of X\n",
+    "model.kde_at_points(bootstrap_curves)\n",
+    "\n",
+    "# Plot with 90% confidence interval\n",
+    "model.plot_with_confidence_band(bootstrap_curves)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09e6bc62",
+   "metadata": {},
+   "source": [
+    "Plot 1: Bootstrap LCLS Estimates\n",
+    "\n",
+    "The spread of gray lines around the red line reflects uncertainty in the model’s predictions.\n",
+    "\n",
+    "For smaller area values, the fits are tighter and more consistent.\n",
+    "\n",
+    "For larger area values (especially beyond ~3500), the fits become erratic due to sparse data, leading to high variability.\n",
+    "\n",
+    "Plot 2: KDE of Bootstrap Predictions at Selected X Quantiles\n",
+    "\n",
+    "Distributions are smooth and unimodal at each quantile.\n",
+    "\n",
+    "Spread decreases slightly as Area increases, suggesting slightly higher model stability in the mid-range.\n",
+    "\n",
+    "Provides insight into prediction uncertainty at specific points along the Area axis.\n",
+    "\n",
+    "Plot 3: Nadaraya-Watson with Epanechnikov Kernel and 90% Confidence Interval\n",
+    "\n",
+    "The CI band is narrowest in the mid-range of the data where the density of points is highest.\n",
+    "\n",
+    "CI widens at the tails, especially beyond Area > 3500, reflecting model uncertainty due to data sparsity.\n",
+    "\n",
+    "The model appears to capture the main trend in the data but behaves erratically in sparse regions."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "DS5030",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/assignment_5_question_4.ipynb b/assignment_5_question_4.ipynb
new file mode 100644
index 0000000..89eec46
--- /dev/null
+++ b/assignment_5_question_4.ipynb
@@ -0,0 +1,810 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dd21efe9",
+   "metadata": {},
+   "source": [
+    "# Assignment 5\n",
+    "### Do all four questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ee9b4e8",
+   "metadata": {},
+   "source": [
+    "**1.** Let's review some basic matrix multiplication. When you have an $M \\times N$ matrix $A$ with $M$ rows and $N$ columns, \n",
+    "$$\n",
+    "A= \\left[ \\begin{array}{cccc} a_{11} & a_{12} & ... & a_{1N} \\\\\n",
+    "a_{21} & a_{22} & ... & a_{2N} \\\\\n",
+    "\\vdots & \\vdots & ... & \\vdots \\\\\n",
+    "a_{M1} & a_{M2} & ... & a_{MN} \n",
+    "\\end{array} \\right],\n",
+    "$$\n",
+    "and you right-multiply it by a vector\n",
+    "$$\n",
+    "x = \\left[ \\begin{array}{c} x_1 \\\\ x_2 \\\\ \\vdots \\\\ x_N \n",
+    "\\end{array} \\right],\n",
+    "$$\n",
+    "you get\n",
+    "$$\n",
+    "Ax = \\left[ \\begin{array}{c} \\sum_{i=1}^N a_{1i} x_i \\\\ \\sum_{i=1}^N a_{2i} x_i \\\\ \\vdots \\\\ \\sum_{i=1}^N a_{Mi} x_i \n",
+    "\\end{array} \\right].\n",
+    "$$\n",
+    "This is just \"matrix row times column vector\" element-by-element, stacking the results into a new vector.\n",
+    "\n",
+    "For this to make sense, $N$ must be the same for the matrix and the vector, but $M$ can be different from $N$. \n",
+    "\n",
+    "Let's play with some NumPy to see this. First we'll define a matrix $A$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "33df3579",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6],\n",
+       "       [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "A = np.array([ [1,2,3],\n",
+    "              [4,5,6],\n",
+    "              [7,8,9]])\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94b1b3ac",
+   "metadata": {},
+   "source": [
+    "a. Multiply $A$ times each of the following vectors using the @ operator. Explain which part of the $A$ matrix gets selected and explain why, using the definition of matrix multiplication. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5b6148d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e_1 = np.array([1,0,0])\n",
+    "e_2 = np.array([0,1,0])\n",
+    "e_3 = np.array([0,0,1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72a4704e",
+   "metadata": {},
+   "source": [
+    "b. Now multiply $A$ times $u = (1,1,1)$. Explain the logic of the result with the definition of matrix multiplication."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5bf73f9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u = np.ones(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dde75eab",
+   "metadata": {},
+   "source": [
+    "c. Whenever a matrix has 1's on the diagonal and zeros everywhere else, we call it an **identity matrix**. What happens when you multiple $A$ times $x$ below? What happens when you multiple an identity matrix times any vector? Explain your result with the definition of matrix multiplication."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b0d349c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([ [1,0,0],\n",
+    "              [0,1,0],\n",
+    "              [0,0,1]])\n",
+    "x = np.array([-2,4,11])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57141925",
+   "metadata": {},
+   "source": [
+    "d. What if every row and column sum to 1, but the 1's are no longer on the diagonal? Multiple $A$ times $X$ below and explain the result. Create another matrix whose rows and columns sum to 1, but is not an identity matrix, and show how it permutes the values of $x$. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "83a9ed11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([ [0,0,1],\n",
+    "              [1,0,0],\n",
+    "              [0,1,0]])\n",
+    "x = np.array([-2,4,11])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "611c26c3",
+   "metadata": {},
+   "source": [
+    "e. The next matrix $A$ could be a Markov transition matrix: Its columns sum to 1, and each entry $a_{ij}$ can be interpreted as the proportion of observations who moved from state $j$ to state $i$. Multiply $A$ by each of the vectors $e_1$, $e_2$, and $e_3$, and explain your results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "aff4fb97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.50052958 0.24049286 0.18358131]\n",
+      " [0.02574731 0.39251588 0.37907577]\n",
+      " [0.47372311 0.36699127 0.43734292]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "rng = np.random.default_rng(100)\n",
+    "A = rng.random((3,3)) # Generate a random 3X3 matrix\n",
+    "sums = np.sum(A,axis=0) # Column sums\n",
+    "A = A/sums # Normalize the columns so they sum to 1\n",
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0647bfa7",
+   "metadata": {},
+   "source": [
+    "f. For each of the vectors $e_1, e_2, e_3$, multiple $A$ times that vector 5 times. What answer do you get for each starting vector? Describe the behavior you observe."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa2c9a93",
+   "metadata": {},
+   "source": [
+    "*2.* Let's consider a simple Markov transition matrix over two states:\n",
+    "$$\n",
+    "T = \\left[ \\begin{array}{cc} p_{1\\leftarrow 1} &  p_{1\\leftarrow 2} \\\\\n",
+    "p_{2 \\leftarrow 1} & p_{2 \\leftarrow 2} \\end{array}\\right] \n",
+    "$$\n",
+    "The arrows help visualize the transition a bit: This is the same index notation as usual, $p_{ij}$, but writing it $p_{i \\leftarrow j}$ emphasizes that it's the proportion of times that state $j$ transitions to state $i$. Below, $T$ is given by\n",
+    "$$\n",
+    "T = \\left[ \\begin{array}{cc} .25 & .5 \\\\\n",
+    ".75 & .5 \\end{array}\\right].\n",
+    "$$\n",
+    "\n",
+    "- Start in state 1, at the initial condition $[1,0]$. Multiply that vector by $T$. Write out the result in terms of the formula and compute the result in a code chunk below. What is this object you're looking at, in terms of proportions and transitions?\n",
+    "- Multiple by $T$ again. What do you get? This isn't a column of $T$. Explain in words what it is. (Hint: A forecast of what in what period?)\n",
+    "- Keep multiplying the current vector of outcomes by $T$. When does it start to settle down without changing further?\n",
+    "- Do the above analysis again, starting from the initial condition $[0,1]$. Do you get a different result?\n",
+    "- The take-away is that, in the long run, these chains settle down into the long-run proportions, and the sensitivity on initial conditions vanishes. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4b6a775f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T = np.array([[ 1/4, 1/2],\n",
+    "                 [ 3/4, 1/2 ]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "921592e9",
+   "metadata": {},
+   "source": [
+    "3. Weather data\n",
+    "\n",
+    "- Load the `cville_weather.csv` data. This includes data from Jan 4, 2024 to Feb 2, 2025. Are there any missing data issues?\n",
+    "- Based on the precipitation variable, `PRCP`, make a new variable called `rain` that takes the value 1 if `PRCP`>0 and 0 otherwise.\n",
+    "- Build a two-state Markov chain over the states 0 and 1 for the `rain` variable. \n",
+    "- For your chain from c, how likely is it to rain if it was rainy yesterday? How likely is it to rain if it was clear yesterday?\n",
+    "- Starting from a clear day, forecast the distribution. How quickly does it converge to a fixed result? What if you start from a rainy day?\n",
+    "- Conditional on being rainy, plot a KDE of the `PRCP` variable.\n",
+    "- Describe one way of making your model better for forecasting and simulation the weather.\n",
+    "\n",
+    "Congratulations, you now are a non-parametric meteorologist!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b953c2c4",
+   "metadata": {},
+   "source": [
+    "4. Taxicab trajectories: Using the pickled taxicab data, we want to complete the exercise from class.\n",
+    "\n",
+    "- For the taxicab trajectory data, determine your state space and clean your sequences of cab rides.\n",
+    "- Compute the transition matrix for the taxicab data between neighborhoods in Manhattan. Plot it in a heat map. What are the most common routes?\n",
+    "- Explain why taxicabs are most likely order 1, and not 2 or more.\n",
+    "- Starting at Hell's Kitchen, create a sequence of forecasts of where the cab is likely to be in 2, 3, 5, and 10 trips\n",
+    "- Starting at any neighborhood, iterate your forecast until it is no longer changing very much. Where do cabs spend most of their time working in Manhattan?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1eb3092e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataFrame shape: (38344, 1000)\n",
+      "First few rows:\n",
+      "             series_0      series_1           series_2           series_3  \\\n",
+      "0  Outside Manhattan       Midtown            Chelsea  Greenwich Village   \n",
+      "1  Outside Manhattan      Kips Bay    Upper East Side        East Harlem   \n",
+      "2  Outside Manhattan      Kips Bay    Upper East Side            Midtown   \n",
+      "3  Outside Manhattan  East Village  Outside Manhattan  Outside Manhattan   \n",
+      "4  Outside Manhattan  East Village  Outside Manhattan  Outside Manhattan   \n",
+      "\n",
+      "            series_4           series_5         series_6         series_7  \\\n",
+      "0  Battery Park City            Midtown  Upper East Side  Upper West Side   \n",
+      "1       East Village            Midtown  Upper East Side  Upper East Side   \n",
+      "2       East Village            Midtown  Upper East Side  Upper East Side   \n",
+      "3           Gramercy  Flatiron District  Upper East Side          Midtown   \n",
+      "4           Gramercy       East Village          Midtown          Midtown   \n",
+      "\n",
+      "            series_8           series_9  ... series_990       series_991  \\\n",
+      "0  Outside Manhattan           Kips Bay  ...    Chelsea          Midtown   \n",
+      "1  Outside Manhattan  Greenwich Village  ...    Chelsea  Stuyvesant Town   \n",
+      "2  Outside Manhattan  Greenwich Village  ...       SoHo  Stuyvesant Town   \n",
+      "3  Outside Manhattan        Murray Hill  ...    Tribeca   Hell's Kitchen   \n",
+      "4  Outside Manhattan            Midtown  ...    Tribeca   Hell's Kitchen   \n",
+      "\n",
+      "        series_992        series_993         series_994       series_995  \\\n",
+      "0          Tribeca   Upper East Side               SoHo  Upper West Side   \n",
+      "1  Upper East Side       Murray Hill  Battery Park City          Midtown   \n",
+      "2  Upper East Side       Murray Hill            Tribeca          Midtown   \n",
+      "3      Murray Hill  Theater District  Greenwich Village      East Harlem   \n",
+      "4      Murray Hill  Theater District       East Village  Upper East Side   \n",
+      "\n",
+      "          series_996         series_997         series_998       series_999  \n",
+      "0  Outside Manhattan       Central Park  Flatiron District          Midtown  \n",
+      "1  Outside Manhattan    Upper East Side     Hell's Kitchen           Nolita  \n",
+      "2  Outside Manhattan    Upper West Side     Hell's Kitchen           Nolita  \n",
+      "3       West Village    Upper West Side            Chelsea  Upper East Side  \n",
+      "4       West Village  Flatiron District            Chelsea  Upper East Side  \n",
+      "\n",
+      "[5 rows x 1000 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load the .pkl file\n",
+    "with open('taxicab.pkl', 'rb') as file:\n",
+    "    data = pickle.load(file)\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "data_reset = [series.reset_index(drop=True) for series in data]\n",
+    "df = pd.concat(data_reset, axis=1, ignore_index=True)\n",
+    "df.columns = [f'series_{i}' for i in range(len(data))]\n",
+    "print(\"DataFrame shape:\", df.shape)\n",
+    "print(\"First few rows:\\n\", df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ac7b6ac",
+   "metadata": {},
+   "source": [
+    "# 1 State Space and Cleaning Sequences\n",
+    "\n",
+    "The state space consists of 38 Manhattan neighborhoods (e.g., Midtown, Upper East Side, Hell's Kitchen) plus 'outside manhattan'. The sequences were cleaned by converting neighborhood names to lowercase, stripping whitespace, and excluding invalid entries. The data, stored in a DataFrame of shape (38344, 1000), was processed to ensure only valid Manhattan neighborhoods were used for analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8c38fb13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "State space (Manhattan neighborhoods): ['central park', 'theater district', 'roosevelt island', 'gramercy', 'lower east side', 'governors island', 'stuyvesant town', 'noho', 'chelsea', 'chinatown', 'west village', 'greenwich village', 'murray hill', 'harlem', 'marble hill', 'inwood', 'two bridges', 'civic center', 'kips bay', 'little italy', 'east harlem', 'east village', 'outside manhattan', 'morningside heights', 'flatiron district', 'washington heights', 'tribeca', 'upper east side', \"randall's island\", 'financial district', \"hell's kitchen\", 'soho', 'nolita', 'ellis island', 'liberty island', 'midtown', 'upper west side', 'battery park city']\n",
+      "Number of states: 38\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get unique neighborhoods from all columns, excluding 'outside manhattan'\n",
+    "all_neighborhoods = set()\n",
+    "for col in df.columns:\n",
+    "    all_neighborhoods.update(df[col].str.lower().str.strip().dropna())\n",
+    "states = list(all_neighborhoods)\n",
+    "S = len(states)\n",
+    "print(\"\\nState space (Manhattan neighborhoods):\", states)\n",
+    "print(\"Number of states:\", S)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44c1ebdd",
+   "metadata": {},
+   "source": [
+    "# 2 Transition Matrix and Heatmap\n",
+    "\n",
+    "The transition matrix was computed, showing probabilities of moving between neighborhoods (column-normalized, so columns sum to 1). A heatmap visualizes these probabilities, with darker shades indicating higher likelihoods. Strong diagonal probabilities (e.g., Midtown: 0.36, Chelsea: 0.33, Upper East Side: 0.46, Upper West Side: 0.42, outside Manhattan: 0.51) indicate cabs frequently stay within these neighborhoods, reflecting high local demand. Significant off-diagonal probabilities, such as Liberty Island -> Gramercy (1.00), Ellis Island -> outside Manhattan (0.33), Governors Island -> outside Manhattan (0.33), and Morningside Heights -> Upper West Side (0.35), show frequent trips between specific areas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c30caeab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Transition Matrix:\n",
+      "                      central park  theater district  roosevelt island  \\\n",
+      "central park                 0.14              0.02              0.01   \n",
+      "theater district             0.04              0.21              0.02   \n",
+      "roosevelt island             0.00              0.00              0.12   \n",
+      "gramercy                     0.01              0.01              0.01   \n",
+      "lower east side              0.00              0.00              0.01   \n",
+      "governors island             0.00              0.00              0.00   \n",
+      "stuyvesant town              0.00              0.00              0.00   \n",
+      "noho                         0.00              0.00              0.00   \n",
+      "chelsea                      0.03              0.09              0.03   \n",
+      "chinatown                    0.00              0.00              0.00   \n",
+      "west village                 0.01              0.02              0.01   \n",
+      "greenwich village            0.01              0.01              0.01   \n",
+      "murray hill                  0.01              0.02              0.02   \n",
+      "harlem                       0.01              0.01              0.00   \n",
+      "marble hill                  0.00              0.00              0.00   \n",
+      "inwood                       0.00              0.00              0.00   \n",
+      "two bridges                  0.00              0.00              0.00   \n",
+      "civic center                 0.00              0.00              0.00   \n",
+      "kips bay                     0.00              0.01              0.02   \n",
+      "little italy                 0.00              0.00              0.00   \n",
+      "east harlem                  0.02              0.01              0.01   \n",
+      "east village                 0.01              0.01              0.02   \n",
+      "outside manhattan            0.03              0.07              0.28   \n",
+      "morningside heights          0.01              0.00              0.00   \n",
+      "flatiron district            0.01              0.01              0.01   \n",
+      "washington heights           0.00              0.00              0.00   \n",
+      "tribeca                      0.00              0.01              0.01   \n",
+      "upper east side              0.20              0.06              0.19   \n",
+      "randall's island             0.00              0.00              0.00   \n",
+      "financial district           0.00              0.01              0.01   \n",
+      "hell's kitchen               0.04              0.12              0.02   \n",
+      "soho                         0.01              0.01              0.01   \n",
+      "nolita                       0.00              0.00              0.00   \n",
+      "ellis island                 0.00              0.00              0.00   \n",
+      "liberty island               0.00              0.00              0.00   \n",
+      "midtown                      0.19              0.21              0.15   \n",
+      "upper west side              0.22              0.07              0.02   \n",
+      "battery park city            0.00              0.01              0.00   \n",
+      "\n",
+      "                     gramercy  lower east side  governors island  \\\n",
+      "central park             0.00             0.00              0.00   \n",
+      "theater district         0.02             0.01              0.00   \n",
+      "roosevelt island         0.00             0.00              0.00   \n",
+      "gramercy                 0.18             0.03              0.08   \n",
+      "lower east side          0.02             0.21              0.00   \n",
+      "governors island         0.00             0.00              0.17   \n",
+      "stuyvesant town          0.02             0.01              0.08   \n",
+      "noho                     0.01             0.02              0.00   \n",
+      "chelsea                  0.10             0.05              0.17   \n",
+      "chinatown                0.00             0.02              0.00   \n",
+      "west village             0.03             0.03              0.00   \n",
+      "greenwich village        0.04             0.03              0.00   \n",
+      "murray hill              0.05             0.02              0.00   \n",
+      "harlem                   0.00             0.00              0.00   \n",
+      "marble hill              0.00             0.00              0.00   \n",
+      "inwood                   0.00             0.00              0.00   \n",
+      "two bridges              0.00             0.00              0.00   \n",
+      "civic center             0.00             0.00              0.00   \n",
+      "kips bay                 0.06             0.03              0.00   \n",
+      "little italy             0.00             0.00              0.00   \n",
+      "east harlem              0.00             0.01              0.00   \n",
+      "east village             0.08             0.15              0.08   \n",
+      "outside manhattan        0.05             0.13              0.33   \n",
+      "morningside heights      0.00             0.00              0.00   \n",
+      "flatiron district        0.03             0.01              0.00   \n",
+      "washington heights       0.00             0.00              0.00   \n",
+      "tribeca                  0.01             0.01              0.00   \n",
+      "upper east side          0.05             0.04              0.00   \n",
+      "randall's island         0.00             0.00              0.00   \n",
+      "financial district       0.01             0.03              0.08   \n",
+      "hell's kitchen           0.02             0.02              0.00   \n",
+      "soho                     0.02             0.03              0.00   \n",
+      "nolita                   0.00             0.02              0.00   \n",
+      "ellis island             0.00             0.00              0.00   \n",
+      "liberty island           0.00             0.00              0.00   \n",
+      "midtown                  0.13             0.06              0.00   \n",
+      "upper west side          0.02             0.02              0.00   \n",
+      "battery park city        0.01             0.01              0.00   \n",
+      "\n",
+      "                     stuyvesant town  noho  chelsea  chinatown  ...  \\\n",
+      "central park                    0.00  0.00     0.01       0.00  ...   \n",
+      "theater district                0.01  0.01     0.04       0.02  ...   \n",
+      "roosevelt island                0.00  0.00     0.00       0.00  ...   \n",
+      "gramercy                        0.09  0.05     0.02       0.02  ...   \n",
+      "lower east side                 0.02  0.02     0.01       0.10  ...   \n",
+      "governors island                0.00  0.00     0.00       0.00  ...   \n",
+      "stuyvesant town                 0.14  0.01     0.00       0.01  ...   \n",
+      "noho                            0.01  0.14     0.01       0.02  ...   \n",
+      "chelsea                         0.07  0.08     0.33       0.05  ...   \n",
+      "chinatown                       0.00  0.01     0.00       0.13  ...   \n",
+      "west village                    0.02  0.05     0.06       0.03  ...   \n",
+      "greenwich village               0.02  0.06     0.03       0.02  ...   \n",
+      "murray hill                     0.04  0.03     0.02       0.01  ...   \n",
+      "harlem                          0.00  0.00     0.00       0.00  ...   \n",
+      "marble hill                     0.00  0.00     0.00       0.00  ...   \n",
+      "inwood                          0.00  0.00     0.00       0.00  ...   \n",
+      "two bridges                     0.00  0.00     0.00       0.00  ...   \n",
+      "civic center                    0.00  0.00     0.00       0.02  ...   \n",
+      "kips bay                        0.09  0.03     0.02       0.02  ...   \n",
+      "little italy                    0.00  0.00     0.00       0.03  ...   \n",
+      "east harlem                     0.01  0.00     0.00       0.00  ...   \n",
+      "east village                    0.10  0.12     0.03       0.08  ...   \n",
+      "outside manhattan               0.05  0.08     0.05       0.11  ...   \n",
+      "morningside heights             0.00  0.00     0.00       0.00  ...   \n",
+      "flatiron district               0.02  0.02     0.03       0.01  ...   \n",
+      "washington heights              0.00  0.00     0.00       0.00  ...   \n",
+      "tribeca                         0.01  0.02     0.01       0.05  ...   \n",
+      "upper east side                 0.06  0.04     0.04       0.02  ...   \n",
+      "randall's island                0.00  0.00     0.00       0.00  ...   \n",
+      "financial district              0.02  0.02     0.01       0.06  ...   \n",
+      "hell's kitchen                  0.02  0.02     0.07       0.02  ...   \n",
+      "soho                            0.01  0.05     0.02       0.06  ...   \n",
+      "nolita                          0.00  0.01     0.00       0.02  ...   \n",
+      "ellis island                    0.00  0.00     0.00       0.00  ...   \n",
+      "liberty island                  0.00  0.00     0.00       0.00  ...   \n",
+      "midtown                         0.13  0.09     0.13       0.06  ...   \n",
+      "upper west side                 0.02  0.02     0.03       0.01  ...   \n",
+      "battery park city               0.00  0.01     0.01       0.01  ...   \n",
+      "\n",
+      "                     randall's island  financial district  hell's kitchen  \\\n",
+      "central park                     0.02                0.00            0.01   \n",
+      "theater district                 0.01                0.02            0.08   \n",
+      "roosevelt island                 0.00                0.00            0.00   \n",
+      "gramercy                         0.00                0.01            0.01   \n",
+      "lower east side                  0.00                0.03            0.01   \n",
+      "governors island                 0.00                0.00            0.00   \n",
+      "stuyvesant town                  0.00                0.01            0.00   \n",
+      "noho                             0.00                0.01            0.00   \n",
+      "chelsea                          0.01                0.05            0.11   \n",
+      "chinatown                        0.00                0.02            0.00   \n",
+      "west village                     0.01                0.04            0.03   \n",
+      "greenwich village                0.00                0.02            0.01   \n",
+      "murray hill                      0.01                0.01            0.01   \n",
+      "harlem                           0.02                0.00            0.01   \n",
+      "marble hill                      0.00                0.00            0.00   \n",
+      "inwood                           0.00                0.00            0.00   \n",
+      "two bridges                      0.00                0.00            0.00   \n",
+      "civic center                     0.00                0.01            0.00   \n",
+      "kips bay                         0.00                0.01            0.01   \n",
+      "little italy                     0.00                0.00            0.00   \n",
+      "east harlem                      0.17                0.00            0.01   \n",
+      "east village                     0.01                0.04            0.01   \n",
+      "outside manhattan                0.17                0.08            0.05   \n",
+      "morningside heights              0.01                0.00            0.00   \n",
+      "flatiron district                0.00                0.01            0.01   \n",
+      "washington heights               0.00                0.00            0.00   \n",
+      "tribeca                          0.00                0.08            0.01   \n",
+      "upper east side                  0.22                0.03            0.05   \n",
+      "randall's island                 0.18                0.00            0.00   \n",
+      "financial district               0.00                0.31            0.01   \n",
+      "hell's kitchen                   0.01                0.02            0.28   \n",
+      "soho                             0.00                0.04            0.01   \n",
+      "nolita                           0.00                0.01            0.00   \n",
+      "ellis island                     0.00                0.00            0.00   \n",
+      "liberty island                   0.00                0.00            0.00   \n",
+      "midtown                          0.06                0.08            0.15   \n",
+      "upper west side                  0.06                0.01            0.10   \n",
+      "battery park city                0.00                0.04            0.01   \n",
+      "\n",
+      "                     soho  nolita  ellis island  liberty island  midtown  \\\n",
+      "central park         0.00    0.00          0.00             0.0     0.01   \n",
+      "theater district     0.02    0.01          0.00             0.0     0.05   \n",
+      "roosevelt island     0.00    0.00          0.00             0.0     0.00   \n",
+      "gramercy             0.02    0.03          0.00             1.0     0.02   \n",
+      "lower east side      0.02    0.05          0.00             0.0     0.01   \n",
+      "governors island     0.00    0.00          0.00             0.0     0.00   \n",
+      "stuyvesant town      0.00    0.01          0.00             0.0     0.00   \n",
+      "noho                 0.02    0.05          0.00             0.0     0.00   \n",
+      "chelsea              0.09    0.06          0.00             0.0     0.07   \n",
+      "chinatown            0.01    0.01          0.00             0.0     0.00   \n",
+      "west village         0.09    0.05          0.00             0.0     0.02   \n",
+      "greenwich village    0.05    0.04          0.00             0.0     0.01   \n",
+      "murray hill          0.02    0.02          0.00             0.0     0.04   \n",
+      "harlem               0.00    0.00          0.00             0.0     0.00   \n",
+      "marble hill          0.00    0.00          0.00             0.0     0.00   \n",
+      "inwood               0.00    0.00          0.00             0.0     0.00   \n",
+      "two bridges          0.00    0.00          0.00             0.0     0.00   \n",
+      "civic center         0.01    0.00          0.00             0.0     0.00   \n",
+      "kips bay             0.01    0.02          0.00             0.0     0.02   \n",
+      "little italy         0.00    0.01          0.33             0.0     0.00   \n",
+      "east harlem          0.00    0.00          0.00             0.0     0.01   \n",
+      "east village         0.05    0.09          0.00             0.0     0.02   \n",
+      "outside manhattan    0.07    0.11          0.33             0.0     0.06   \n",
+      "morningside heights  0.00    0.00          0.00             0.0     0.00   \n",
+      "flatiron district    0.02    0.01          0.00             0.0     0.02   \n",
+      "washington heights   0.00    0.00          0.00             0.0     0.00   \n",
+      "tribeca              0.04    0.02          0.33             0.0     0.01   \n",
+      "upper east side      0.03    0.03          0.00             0.0     0.13   \n",
+      "randall's island     0.00    0.00          0.00             0.0     0.00   \n",
+      "financial district   0.03    0.02          0.00             0.0     0.01   \n",
+      "hell's kitchen       0.02    0.02          0.00             0.0     0.05   \n",
+      "soho                 0.25    0.09          0.00             0.0     0.01   \n",
+      "nolita               0.02    0.16          0.00             0.0     0.00   \n",
+      "ellis island         0.00    0.00          0.00             0.0     0.00   \n",
+      "liberty island       0.00    0.00          0.00             0.0     0.00   \n",
+      "midtown              0.08    0.07          0.00             0.0     0.36   \n",
+      "upper west side      0.02    0.02          0.00             0.0     0.05   \n",
+      "battery park city    0.02    0.01          0.00             0.0     0.00   \n",
+      "\n",
+      "                     upper west side  battery park city  \n",
+      "central park                    0.03               0.00  \n",
+      "theater district                0.04               0.02  \n",
+      "roosevelt island                0.00               0.00  \n",
+      "gramercy                        0.00               0.01  \n",
+      "lower east side                 0.00               0.01  \n",
+      "governors island                0.00               0.00  \n",
+      "stuyvesant town                 0.00               0.00  \n",
+      "noho                            0.00               0.00  \n",
+      "chelsea                         0.04               0.07  \n",
+      "chinatown                       0.00               0.01  \n",
+      "west village                    0.01               0.06  \n",
+      "greenwich village               0.01               0.02  \n",
+      "murray hill                     0.01               0.01  \n",
+      "harlem                          0.02               0.00  \n",
+      "marble hill                     0.00               0.00  \n",
+      "inwood                          0.00               0.00  \n",
+      "two bridges                     0.00               0.00  \n",
+      "civic center                    0.00               0.00  \n",
+      "kips bay                        0.00               0.01  \n",
+      "little italy                    0.00               0.00  \n",
+      "east harlem                     0.01               0.00  \n",
+      "east village                    0.01               0.02  \n",
+      "outside manhattan               0.04               0.05  \n",
+      "morningside heights             0.02               0.00  \n",
+      "flatiron district               0.00               0.01  \n",
+      "washington heights              0.01               0.00  \n",
+      "tribeca                         0.00               0.12  \n",
+      "upper east side                 0.11               0.02  \n",
+      "randall's island                0.00               0.00  \n",
+      "financial district              0.00               0.09  \n",
+      "hell's kitchen                  0.08               0.03  \n",
+      "soho                            0.00               0.05  \n",
+      "nolita                          0.00               0.00  \n",
+      "ellis island                    0.00               0.00  \n",
+      "liberty island                  0.00               0.00  \n",
+      "midtown                         0.12               0.07  \n",
+      "upper west side                 0.42               0.02  \n",
+      "battery park city               0.00               0.26  \n",
+      "\n",
+      "[38 rows x 38 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Most common routes:\n",
+      "liberty island -> gramercy: 1.00\n",
+      "outside manhattan -> outside manhattan: 0.51\n",
+      "upper east side -> upper east side: 0.46\n",
+      "upper west side -> upper west side: 0.42\n",
+      "midtown -> midtown: 0.36\n",
+      "morningside heights -> upper west side: 0.35\n",
+      "chelsea -> chelsea: 0.33\n",
+      "ellis island -> outside manhattan: 0.33\n",
+      "governors island -> outside manhattan: 0.33\n",
+      "ellis island -> little italy: 0.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "tr_counts = np.zeros((S, S))\n",
+    "for col in df.columns:\n",
+    "    col_seq = df[col].str.lower().str.strip().dropna()\n",
+    "    col_seq = col_seq[col_seq.isin(all_neighborhoods)].values\n",
+    "    for t in range(1, len(col_seq)):\n",
+    "        if col_seq[t-1] in states and col_seq[t] in states:\n",
+    "            index_from = states.index(col_seq[t-1])\n",
+    "            index_to = states.index(col_seq[t])\n",
+    "            tr_counts[index_to, index_from] += 1\n",
+    "\n",
+    "# Transition probabilities (rows sum to 1)\n",
+    "sums = tr_counts.sum(axis=0, keepdims=True)\n",
+    "tr_pr = np.divide(tr_counts, sums, out=np.zeros_like(tr_counts), where=sums!=0)\n",
+    "tr_df = pd.DataFrame(np.round(tr_pr, 2), index=states, columns=states)\n",
+    "print(\"\\nTransition Matrix:\\n\", tr_df)\n",
+    "\n",
+    "# Plot heatmap\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "sns.heatmap(tr_pr, cmap='Blues', square=True, xticklabels=states, yticklabels=states,\n",
+    "            cbar_kws={'label': 'Transition Probability'})\n",
+    "plt.title('Taxicab Transition Probabilities')\n",
+    "plt.xlabel('To Neighborhood')\n",
+    "plt.ylabel('From Neighborhood')\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Most common routes\n",
+    "print(\"\\nMost common routes:\")\n",
+    "tr_flat = tr_pr.flatten()\n",
+    "top_indices = np.argsort(tr_flat)[-10:][::-1]\n",
+    "for idx in top_indices:\n",
+    "    i, j = divmod(idx, S)\n",
+    "    print(f\"{states[j]} -> {states[i]}: {tr_pr[i, j]:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea10c929",
+   "metadata": {},
+   "source": [
+    "# 3 Why Taxicabs Are Likely Order 1\n",
+    "\n",
+    "Taxicabs are modeled as a first-order Markov chain because the next destination depends primarily on the current location and immediate factors like passenger demand or proximity, not prior trip history. Higher-order models (e.g., order 2) are unnecessary as they add complexity without significant predictive gain, given that passenger requests and driver decisions are largely independent of earlier trips."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58c67502",
+   "metadata": {},
+   "source": [
+    "# 4 Forecasting from Hell’s Kitchen\n",
+    "\n",
+    "Starting from Hell’s Kitchen, the probability distribution after 2, 3, 5, and 10 trips was computed using the transition matrix. After 2 trips, the top neighborhoods are Midtown (0.17), Hell’s Kitchen (0.12), and Chelsea (0.11). By 10 trips, the distribution stabilizes, with Midtown (0.17), Upper East Side (0.12), and outside Manhattan (0.10) being the most likely, indicating a shift toward high-demand areas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0acf94e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Forecasts from Hell's Kitchen:\n",
+      "After 2 trips: {'midtown': np.float64(0.17), \"hell's kitchen\": np.float64(0.12), 'chelsea': np.float64(0.11)}\n",
+      "After 3 trips: {'midtown': np.float64(0.17), 'upper east side': np.float64(0.11), 'chelsea': np.float64(0.1)}\n",
+      "After 5 trips: {'midtown': np.float64(0.17), 'upper east side': np.float64(0.12), 'outside manhattan': np.float64(0.1)}\n",
+      "After 10 trips: {'midtown': np.float64(0.17), 'upper east side': np.float64(0.12), 'outside manhattan': np.float64(0.1)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    hk_idx = states.index(\"hell's kitchen\")\n",
+    "except ValueError:\n",
+    "    print(\"Warning: Hell's Kitchen not in states. Using first state instead.\")\n",
+    "    hk_idx = 0\n",
+    "initial = np.zeros(S)\n",
+    "initial[hk_idx] = 1\n",
+    "print(\"\\nForecasts from Hell's Kitchen:\")\n",
+    "vec = initial\n",
+    "for i in range(10):\n",
+    "    vec = tr_pr @ vec\n",
+    "    if i+1 in [2, 3, 5, 10]:\n",
+    "        top3 = np.argsort(vec)[-3:][::-1]\n",
+    "        print(f\"After {i+1} trips:\", {states[idx]: round(vec[idx], 2) for idx in top3})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8bd9a6b",
+   "metadata": {},
+   "source": [
+    "# 5 Long-Run Distribution\n",
+    "\n",
+    "Iterating from a uniform distribution, the Markov chain converged after 7 steps to a stationary distribution. Cabs spend the most time in Midtown (0.17), Upper East Side (0.12), outside Manhattan (0.10), Chelsea (0.09), and Upper West Side (0.08), reflecting high passenger demand and trip frequency in central and popular neighborhoods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ddf2b4c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Converged at step 7\n",
+      "Long-run distribution (top 5 neighborhoods): {'midtown': np.float64(0.17), 'upper east side': np.float64(0.12), 'outside manhattan': np.float64(0.1), 'chelsea': np.float64(0.09), 'upper west side': np.float64(0.08)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "vec = np.ones(S) / S\n",
+    "for i in range(50):\n",
+    "    vec_new = tr_pr @ vec\n",
+    "    if np.max(np.abs(vec_new - vec)) < 0.001:\n",
+    "        print(f\"\\nConverged at step {i+1}\")\n",
+    "        break\n",
+    "    vec = vec_new\n",
+    "top5 = np.argsort(vec)[-5:][::-1]\n",
+    "print(\"Long-run distribution (top 5 neighborhoods):\", {states[idx]: round(vec[idx], 2) for idx in top5})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "DS5030",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}