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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r-G_BpFaLoa4",
"outputId": "6ce2e622-9704-47a9-a54d-17ea18432dfd"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
"Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n",
"Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.12/dist-packages (3.3.2)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2026.1)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
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"Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.0)\n",
"Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from vaderSentiment) (2.32.4)\n",
"Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.16.3)\n",
"Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.5.3)\n",
"Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
"Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (3.4.7)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (3.11)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (2.5.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (2026.2.25)\n"
]
}
],
"source": [
"# --- 0. INSTALL DEPENDENCIES ---\n",
"!pip install pandas numpy matplotlib seaborn vaderSentiment scikit-learn\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"import seaborn as sns\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
"\n",
"# \u2500\u2500 Styling \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"PALETTE = [\"#2E4057\", \"#048A81\", \"#54C6EB\", \"#EFD28D\", \"#C84B31\"]\n",
"sns.set_theme(style=\"whitegrid\", palette=PALETTE)\n",
"plt.rcParams.update({\"figure.dpi\": 130, \"axes.titlesize\": 13,\n",
" \"axes.labelsize\": 11, \"font.family\": \"DejaVu Sans\"})"
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"N_RIDES = 1000\n",
"cities = [\"Berlin\", \"Munich\", \"Hamburg\", \"Cologne\"]\n",
"ride_types = [\"Standard\", \"Premium\", \"XL\", \"Eco\"]\n",
"time_slots = [\"Morning (6-10)\", \"Midday (10-14)\", \"Afternoon (14-18)\", \"Evening (18-22)\"]\n",
"\n",
"# SECTION 1 \u2013 SIMULATE \"SCRAPED / FOUND\" REAL-WORLD DATA\n",
"# (In production: replace with actual web-scraped or API-fetched CSVs)\n",
"# \u2500\u2500 1a. Ride-level transaction data (quantitative) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"ride_data = pd.DataFrame({\n",
" \"ride_id\": range(1, N_RIDES + 1),\n",
" \"city\": np.random.choice(cities, N_RIDES),\n",
" \"ride_type\": np.random.choice(ride_types, N_RIDES, p=[0.40, 0.30, 0.20, 0.10]),\n",
" \"time_slot\": np.random.choice(time_slots, N_RIDES, p=[0.25, 0.30, 0.30, 0.15]),\n",
" \"distance_km\": np.round(np.random.exponential(4, N_RIDES) + 0.5, 2),\n",
" \"duration_min\": np.round(np.random.normal(18, 6, N_RIDES).clip(3), 1),\n",
" \"base_price_eur\":np.round(np.random.uniform(1.5, 8.0, N_RIDES), 2),\n",
" \"discount_pct\": np.random.choice([0, 5, 10, 15, 20], N_RIDES,\n",
" p=[0.50, 0.20, 0.15, 0.10, 0.05]),\n",
" \"rating\": np.random.choice([1, 2, 3, 4, 5], N_RIDES,\n",
" p=[0.03, 0.07, 0.15, 0.40, 0.35]),\n",
" \"cancelled\": np.random.choice([0, 1], N_RIDES, p=[0.93, 0.07]),\n",
"})\n",
"\n",
"# Introduce 3 % missing values in price and rating (realistic)\n",
"for col in [\"base_price_eur\", \"rating\"]:\n",
" ride_data.loc[ride_data.sample(frac=0.03).index, col] = np.nan\n",
"\n",
"# Derived fields\n",
"ride_data[\"final_price_eur\"] = np.round(\n",
" ride_data[\"base_price_eur\"] * (1 - ride_data[\"discount_pct\"] / 100), 2)\n",
"ride_data[\"price_per_km\"] = np.round(\n",
" ride_data[\"final_price_eur\"] / ride_data[\"distance_km\"], 3)"
],
"metadata": {
"id": "gtbUjaaWMfH-"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# \u2500\u2500 1b. App-review data (qualitative) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"positive_reviews = [\n",
" \"Absolutely love the e-scooter! Fast, clean, affordable.\",\n",
" \"Seamless booking and the bike was in great condition.\",\n",
" \"Best way to get around the city. Highly recommend!\",\n",
" \"Super convenient, saved me 20 minutes every morning.\",\n",
" \"Eco-friendly and cheap. Will use every day.\",\n",
" \"App works perfectly. Scooter was fully charged.\",\n",
" \"Great service, prices are very fair for the distance.\",\n",
" \"Customer support was helpful and friendly.\",\n",
"]\n",
"negative_reviews = [\n",
" \"The scooter was broken when I unlocked it. Very frustrating.\",\n",
" \"Overcharged for a 2 km ride. Pricing is confusing.\",\n",
" \"App crashed three times before I could complete the booking.\",\n",
" \"Terrible availability in my neighbourhood. Always empty.\",\n",
" \"The e-bike seat was damaged and uncomfortable.\",\n",
" \"Hidden fees are unacceptable. Totally misleading pricing.\",\n",
" \"Waited 10 minutes to connect to a scooter. Wasted my time.\",\n",
" \"No customer support response after a billing error.\",\n",
"]\n",
"neutral_reviews = [\n",
" \"It was okay. Nothing special, works as expected.\",\n",
" \"Decent ride, though a bit pricey compared to the metro.\",\n",
" \"Average experience. Some improvements needed in the app.\",\n",
" \"Not bad, but parking zones need to be clearer.\",\n",
" \"Works fine most of the time. Occasional glitches.\",\n",
"]\n",
"\n",
"N_REVIEWS = 1500 # Define N_REVIEWS here\n",
"all_reviews = positive_reviews * 30 + negative_reviews * 20 + neutral_reviews * 10\n",
"review_data = pd.DataFrame({\n",
" \"review_id\": range(1, N_REVIEWS + 1),\n",
" \"city\": np.random.choice(cities, N_REVIEWS),\n",
" \"ride_type\": np.random.choice(ride_types, N_REVIEWS),\n",
" \"review_text\":np.random.choice(all_reviews, N_REVIEWS),\n",
" \"review_date\":pd.date_range(\"2024-01-01\", periods=N_REVIEWS, freq=\"14h\"),\n",
"})\n",
"\n",
"print(\"\u2705 Data generated\")\n",
"print(f\" ride_data : {ride_data.shape}\")\n",
"print(f\" review_data: {review_data.shape}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CzPxA0rAoaHZ",
"outputId": "5174afc6-c010-4b9a-9dc8-8f41a6ac4b56"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u2705 Data generated\n",
" ride_data : (1000, 12)\n",
" review_data: (1500, 5)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 2 \u2013 DATA CLEANING\n",
"# =============================================================================\n",
"\n",
"# \u2500\u2500 2a. Ride data \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"print(\"\\n\u2500\u2500 Missing values BEFORE cleaning \u2500\u2500\")\n",
"print(ride_data[[\"base_price_eur\", \"rating\"]].isnull().sum())\n",
"\n",
"ride_data[\"base_price_eur\"] = ride_data[\"base_price_eur\"].fillna(ride_data[\"base_price_eur\"].median())\n",
"ride_data[\"rating\"] = ride_data[\"rating\"].fillna(round(ride_data[\"rating\"].median()))\n",
"\n",
"print(\"\u2500\u2500 Missing values AFTER cleaning \u2500\u2500\")\n",
"print(ride_data[[\"base_price_eur\", \"rating\"]].isnull().sum())\n",
"\n",
"# Remove duplicate ride IDs (none expected, but good practice)\n",
"ride_data.drop_duplicates(subset=\"ride_id\", inplace=True)\n",
"\n",
"# Drop rides with physically impossible distance\n",
"ride_data = ride_data[ride_data[\"distance_km\"] > 0]\n",
"\n",
"# \u2500\u2500 2b. Review data \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"review_data.dropna(subset=[\"review_text\"], inplace=True)\n",
"review_data[\"review_text\"] = review_data[\"review_text\"].str.strip()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lnGYrVG7o0aO",
"outputId": "be8fb6b5-a485-4495-8ebd-240e741a8ce4"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\u2500\u2500 Missing values BEFORE cleaning \u2500\u2500\n",
"base_price_eur 30\n",
"rating 30\n",
"dtype: int64\n",
"\u2500\u2500 Missing values AFTER cleaning \u2500\u2500\n",
"base_price_eur 0\n",
"rating 0\n",
"dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 3 \u2013 VADER SENTIMENT ANALYSIS ON REVIEWS\n",
"# =============================================================================\n",
"\n",
"!pip install vaderSentiment\n",
"from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
"\n",
"analyzer = SentimentIntensityAnalyzer()\n",
"\n",
"def classify_sentiment(text):\n",
" score = analyzer.polarity_scores(text)[\"compound\"]\n",
" if score >= 0.05: return \"Positive\"\n",
" elif score <= -0.05: return \"Negative\"\n",
" else: return \"Neutral\"\n",
"\n",
"review_data[\"compound_score\"] = review_data[\"review_text\"].apply(\n",
" lambda t: analyzer.polarity_scores(t)[\"compound\"])\n",
"review_data[\"sentiment\"] = review_data[\"review_text\"].apply(classify_sentiment)\n",
"\n",
"print(\"\\n\u2500\u2500 Sentiment distribution \u2500\u2500\")\n",
"print(review_data[\"sentiment\"].value_counts())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iXvqwQRxo-W6",
"outputId": "39694265-24f5-44e0-bc33-0f5300b1b917"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting vaderSentiment\n",
" Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl.metadata (572 bytes)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from vaderSentiment) (2.32.4)\n",
"Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (3.4.7)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (3.11)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (2.5.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (2026.2.25)\n",
"Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
"\u001b[2K \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m126.0/126.0 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: vaderSentiment\n",
"Successfully installed vaderSentiment-3.3.2\n",
"\n",
"\u2500\u2500 Sentiment distribution \u2500\u2500\n",
"sentiment\n",
"Positive 798\n",
"Negative 509\n",
"Neutral 193\n",
"Name: count, dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 4 \u2013 MERGING DATASETS\n",
"# Aggregate rides per (city, ride_type) \u2192 merge with review sentiments\n",
"# =============================================================================\n",
"\n",
"ride_agg = ride_data.groupby([\"city\", \"ride_type\"]).agg(\n",
" total_rides = (\"ride_id\", \"count\"),\n",
" avg_final_price = (\"final_price_eur\", \"mean\"),\n",
" avg_distance_km = (\"distance_km\", \"mean\"),\n",
" avg_rating = (\"rating\", \"mean\"),\n",
" cancellation_rate = (\"cancelled\", \"mean\"),\n",
" avg_price_per_km = (\"price_per_km\", \"mean\"),\n",
").round(3).reset_index()\n",
"\n",
"review_agg = review_data.groupby([\"city\", \"ride_type\"]).agg(\n",
" total_reviews = (\"review_id\", \"count\"),\n",
" avg_compound_score = (\"compound_score\", \"mean\"),\n",
" pct_positive = (\"sentiment\",\n",
" lambda x: (x == \"Positive\").sum() / len(x) * 100),\n",
").round(3).reset_index()\n",
"\n",
"df = pd.merge(ride_agg, review_agg, on=[\"city\", \"ride_type\"], how=\"inner\")\n",
"\n",
"print(\"\\n\u2500\u2500 Merged dataframe head \u2500\u2500\")\n",
"print(df.head(10).to_string(index=False))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7LFPJo32q3Yy",
"outputId": "db4f5adc-b5b1-4f1b-8e50-f086e9bc8a21"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\u2500\u2500 Merged dataframe head \u2500\u2500\n",
" city ride_type total_rides avg_final_price avg_distance_km avg_rating cancellation_rate avg_price_per_km total_reviews avg_compound_score pct_positive\n",
" Berlin Eco 27 4.723 5.653 4.185 0.037 1.940 80 0.226 62.500\n",
" Berlin Premium 70 4.560 4.137 3.943 0.071 2.449 103 0.129 50.485\n",
" Berlin Standard 96 4.485 4.609 4.052 0.073 1.929 101 0.232 57.426\n",
" Berlin XL 55 4.393 4.182 3.909 0.055 2.499 93 0.178 52.688\n",
"Cologne Eco 30 4.920 3.651 4.067 0.067 2.082 97 0.285 60.825\n",
"Cologne Premium 66 4.182 4.683 3.773 0.136 1.496 100 0.123 53.000\n",
"Cologne Standard 90 4.520 4.614 3.856 0.044 2.071 93 0.106 51.613\n",
"Cologne XL 58 4.483 4.732 4.069 0.052 1.594 93 0.022 40.860\n",
"Hamburg Eco 29 4.459 3.848 4.069 0.069 2.212 91 0.152 56.044\n",
"Hamburg Premium 77 4.546 4.655 3.987 0.039 1.923 103 0.182 53.398\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 5 \u2013 EXPLORATORY DATA ANALYSIS (6 charts)\n",
"# =============================================================================\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"\n",
"# Define PALETTE for styling (moved from initial setup to ensure availability)\n",
"PALETTE = [\"#2E4057\", \"#048A81\", \"#54C6EB\", \"#EFD28D\", \"#C84B31\"]\n",
"\n",
"fig = plt.figure(figsize=(20, 22))\n",
"fig.suptitle(\"Urban Mobility Startup \u2013 Exploratory Data Analysis\",\n",
" fontsize=17, fontweight=\"bold\", y=0.98)\n",
"gs = gridspec.GridSpec(3, 2, figure=fig, hspace=0.45, wspace=0.35)\n",
"\n",
"# \u2500\u2500 Chart 1: Average final price by ride type \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"ax1 = fig.add_subplot(gs[0, 0])\n",
"price_by_type = ride_data.groupby(\"ride_type\")[\"final_price_eur\"].mean().sort_values()\n",
"bars = ax1.barh(price_by_type.index, price_by_type.values,\n",
" color=PALETTE[:len(price_by_type)])\n",
"ax1.bar_label(bars, fmt=\"\u20ac%.2f\", padding=4, fontsize=9)\n",
"ax1.set_title(\"Avg. Final Price by Ride Type\")\n",
"ax1.set_xlabel(\"EUR\")\n",
"ax1.set_xlim(0, price_by_type.max() * 1.25)\n",
"\n",
"# \u2500\u2500 Chart 2: Rating distribution \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"ax2 = fig.add_subplot(gs[0, 1])\n",
"rating_counts = ride_data[\"rating\"].value_counts().sort_index()\n",
"ax2.bar(rating_counts.index, rating_counts.values,\n",
" color=PALETTE[1], edgecolor=\"white\", linewidth=0.8)\n",
"ax2.set_title(\"Ride Rating Distribution\")\n",
"ax2.set_xlabel(\"Stars\")\n",
"ax2.set_ylabel(\"Number of Rides\")\n",
"ax2.set_xticks([1, 2, 3, 4, 5])\n",
"\n",
"# \u2500\u2500 Chart 3: Sentiment breakdown by city \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"ax3 = fig.add_subplot(gs[1, 0])\n",
"sent_city = review_data.groupby([\"city\", \"sentiment\"]).size().unstack(fill_value=0)\n",
"sent_city_pct = sent_city.div(sent_city.sum(axis=1), axis=0) * 100\n",
"sent_city_pct[[\"Positive\", \"Neutral\", \"Negative\"]].plot(\n",
" kind=\"bar\", ax=ax3, color=[PALETTE[1], PALETTE[3], PALETTE[4]],\n",
" edgecolor=\"white\", linewidth=0.5)\n",
"ax3.set_title(\"Review Sentiment by City (%)\")\n",
"ax3.set_xlabel(\"\")\n",
"ax3.set_ylabel(\"Share (%)\")\n",
"ax3.legend(title=\"Sentiment\", fontsize=8)\n",
"ax3.tick_params(axis=\"x\", rotation=30)\n",
"\n",
"# \u2500\u2500 Chart 4: Price per km vs avg rating (scatter) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"ax4 = fig.add_subplot(gs[1, 1])\n",
"for i, rt in enumerate(ride_types):\n",
" sub = ride_data[ride_data[\"ride_type\"] == rt]\n",
" ax4.scatter(sub[\"price_per_km\"], sub[\"rating\"] +\n",
" np.random.uniform(-0.1, 0.1, len(sub)),\n",
" label=rt, alpha=0.4, s=14, color=PALETTE[i % len(PALETTE)])\n",
"ax4.set_title(\"Price-per-km vs. Ride Rating\")\n",
"ax4.set_xlabel(\"Price per km (\u20ac)\")\n",
"ax4.set_ylabel(\"Rating (jittered)\")\n",
"ax4.legend(fontsize=8, markerscale=1.5)\n",
"\n",
"# \u2500\u2500 Chart 5: Cancellation rate by time slot \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"ax5 = fig.add_subplot(gs[2, 0])\n",
"cancel_slot = ride_data.groupby(\"time_slot\")[\"cancelled\"].mean().sort_values() * 100\n",
"ax5.bar(cancel_slot.index, cancel_slot.values,\n",
" color=PALETTE[4], edgecolor=\"white\")\n",
"ax5.set_title(\"Cancellation Rate by Time Slot (%)\")\n",
"ax5.set_ylabel(\"Cancellation Rate (%)\")\n",
"ax5.set_ylim(0, cancel_slot.max() * 1.4)\n",
"for p, v in zip(cancel_slot.index, cancel_slot.values):\n",
" ax5.text(p, v + 0.1, f\"{v:.1f}%\", ha=\"center\", fontsize=9)\n",
"\n",
"# \u2500\u2500 Chart 6: Avg compound sentiment score by ride type \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"ax6 = fig.add_subplot(gs[2, 1])\n",
"sent_type = review_data.groupby(\"ride_type\")[\"compound_score\"].mean().sort_values()\n",
"colors_sent = [PALETTE[1] if v >= 0 else PALETTE[4] for v in sent_type.values]\n",
"ax6.barh(sent_type.index, sent_type.values, color=colors_sent)\n",
"ax6.axvline(0, color=\"black\", linewidth=0.8, linestyle=\"--\")\n",
"ax6.set_title(\"Avg. VADER Sentiment Score by Ride Type\")\n",
"ax6.set_xlabel(\"Compound Score (\u22121 to +1)\")\n",
"\n",
"plt.savefig(\"notebook1_eda_output.png\", bbox_inches=\"tight\")\n",
"plt.close()\n",
"print(\"\\n\u2705 EDA chart saved \u2192 notebook1_eda_output.png\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "k3o219Voq9l_",
"outputId": "e5065a98-9ee5-4215-f1b2-58da89a93a67"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\u2705 EDA chart saved \u2192 notebook1_eda_output.png\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# SECTION 6 \u2013 SAVE CLEANED DATASETS FOR NOTEBOOK 2\n",
"# =============================================================================\n",
"ride_data.to_csv(\"ride_data_clean.csv\", index=False)\n",
"review_data.to_csv(\"review_data_clean.csv\", index=False)\n",
"df.to_csv(\"merged_summary.csv\", index=False)\n",
"print(\"\u2705 CSVs saved: ride_data_clean.csv | review_data_clean.csv | merged_summary.csv\")\n",
"\n",
"print(\"\\n\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\")\n",
"print(\" NOTEBOOK 1 COMPLETE\")\n",
"print(\"\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Atl1ma1HsOE6",
"outputId": "78c7f16c-7d69-40a2-e366-bb7e0aa7a255"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u2705 CSVs saved: ride_data_clean.csv | review_data_clean.csv | merged_summary.csv\n",
"\n",
"\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n",
" NOTEBOOK 1 COMPLETE\n",
"\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n"
]
}
]
}
]
} |