diff --git "a/data_analysis.ipynb" "b/data_analysis.ipynb"
new file mode 100644--- /dev/null
+++ "b/data_analysis.ipynb"
@@ -0,0 +1,813 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "175f1599",
+   "metadata": {},
+   "source": [
+    "# **FrodoBots Gaming Dataset**\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02a01f1c",
+   "metadata": {},
+   "source": [
+    "## Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bdbda93",
+   "metadata": {},
+   "source": [
+    "Find all control files in \"data_260523\" file, concat all control files into one table and save as \"combined_control.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2c7cebdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "combined_control_df = pd.DataFrame()\n",
+    "\n",
+    "# Path to data_260523 directory\n",
+    "data_directory = './data/data_260523'\n",
+    "\n",
+    "# Iterate through all directories and subdirectories\n",
+    "for root, dirs, files in os.walk(data_directory):\n",
+    "    for file in files:\n",
+    "        # Check if the file is a control file\n",
+    "        if file.startswith('control'):\n",
+    "            # Full file path\n",
+    "            file_path = os.path.join(root, file)\n",
+    "            # Read the csv file into a dataframe\n",
+    "            try:\n",
+    "                df = pd.read_csv(file_path, on_bad_lines='skip')\n",
+    "            except Exception as e:\n",
+    "                print(f\"Error reading file {file_path}: {e}\")\n",
+    "                continue\n",
+    "            # Parse directory path to get the robot id and session id\n",
+    "            path_parts = root.split('/')\n",
+    "            robot_id = path_parts[-2]\n",
+    "            session_id = path_parts[-1]\n",
+    "            # Add robot id and session id to the dataframe\n",
+    "            df['Robot_ID'] = robot_id\n",
+    "            df['Session'] = session_id\n",
+    "            # Concatenate the dataframe with the combined dataframe\n",
+    "            combined_control_df = pd.concat([combined_control_df, df])\n",
+    "\n",
+    "# Save the combined dataframe as a csv file\n",
+    "combined_control_df.to_csv('./data/combined_control.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "64e6d8be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<div>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>speed</th>\n",
+       "      <th>angular</th>\n",
+       "      <th>rpm_1</th>\n",
+       "      <th>rpm_2</th>\n",
+       "      <th>rpm_3</th>\n",
+       "      <th>rpm_4</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>Robot_ID</th>\n",
+       "      <th>Session</th>\n",
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+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8468211</th>\n",
+       "      <td>0.00</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.684464e+09</td>\n",
+       "      <td>frodobot31cdee</td>\n",
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+       "    </tr>\n",
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+       "      <td>20230519033727</td>\n",
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+       "      <td>frodobot31cdee</td>\n",
+       "      <td>20230519033727</td>\n",
+       "    </tr>\n",
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+       "      <td>20230519033727</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8468216 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         speed  angular  rpm_1  rpm_2  rpm_3  rpm_4     timestamp  \\\n",
+       "0         0.45      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "1         0.00      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "2         0.00      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "3         0.00      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "4         0.00      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "...        ...      ...    ...    ...    ...    ...           ...   \n",
+       "8468211   0.00      0.0    0.0    0.0    0.0    0.0  1.684464e+09   \n",
+       "8468212   0.00      0.0    0.0    0.0    0.0    0.0  1.684464e+09   \n",
+       "8468213   0.00      0.0    0.0    0.0    0.0    0.0  1.684464e+09   \n",
+       "8468214   0.00      0.0    0.0    0.0    0.0    0.0  1.684464e+09   \n",
+       "8468215    NaN      NaN    NaN    NaN    NaN    NaN           NaN   \n",
+       "\n",
+       "               Robot_ID         Session  \n",
+       "0        frodobot2e6a30  20230404102851  \n",
+       "1        frodobot2e6a30  20230404102851  \n",
+       "2        frodobot2e6a30  20230404102851  \n",
+       "3        frodobot2e6a30  20230404102851  \n",
+       "4        frodobot2e6a30  20230404102851  \n",
+       "...                 ...             ...  \n",
+       "8468211  frodobot31cdee  20230519033727  \n",
+       "8468212  frodobot31cdee  20230519033727  \n",
+       "8468213  frodobot31cdee  20230519033727  \n",
+       "8468214  frodobot31cdee  20230519033727  \n",
+       "8468215  frodobot31cdee  20230519033727  \n",
+       "\n",
+       "[8468216 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read combined_control_df\n",
+    "import pandas as pd\n",
+    "\n",
+    "combined_control_df = pd.read_csv('./data/combined_control.csv')\n",
+    "combined_control_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "45338885",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th></th>\n",
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+       "      <th>rpm_3</th>\n",
+       "      <th>rpm_4</th>\n",
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+       "      <th>Session</th>\n",
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+       "      <td>frodobot31cdee</td>\n",
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+       "      <td>frodobot31cdee</td>\n",
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+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8468216 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         speed  angular  rpm_1  rpm_2  rpm_3  rpm_4     timestamp  \\\n",
+       "0         0.45      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "1         0.00      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "2         0.00      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "3         0.00      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "4         0.00      0.0    0.0    0.0    0.0    0.0  1.680601e+09   \n",
+       "...        ...      ...    ...    ...    ...    ...           ...   \n",
+       "8468211   0.00      0.0    0.0    0.0    0.0    0.0  1.684464e+09   \n",
+       "8468212   0.00      0.0    0.0    0.0    0.0    0.0  1.684464e+09   \n",
+       "8468213   0.00      0.0    0.0    0.0    0.0    0.0  1.684464e+09   \n",
+       "8468214   0.00      0.0    0.0    0.0    0.0    0.0  1.684464e+09   \n",
+       "8468215    NaN      NaN    NaN    NaN    NaN    NaN           NaN   \n",
+       "\n",
+       "               Robot_ID         Session  actual_speed  \n",
+       "0        frodobot2e6a30  20230404102851           0.0  \n",
+       "1        frodobot2e6a30  20230404102851           0.0  \n",
+       "2        frodobot2e6a30  20230404102851           0.0  \n",
+       "3        frodobot2e6a30  20230404102851           0.0  \n",
+       "4        frodobot2e6a30  20230404102851           0.0  \n",
+       "...                 ...             ...           ...  \n",
+       "8468211  frodobot31cdee  20230519033727           0.0  \n",
+       "8468212  frodobot31cdee  20230519033727           0.0  \n",
+       "8468213  frodobot31cdee  20230519033727           0.0  \n",
+       "8468214  frodobot31cdee  20230519033727           0.0  \n",
+       "8468215  frodobot31cdee  20230519033727           NaN  \n",
+       "\n",
+       "[8468216 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate \"actual_speed\" and add to \"combined_control_df\"\n",
+    "combined_control_df['actual_speed'] = (combined_control_df['rpm_1'] + combined_control_df['rpm_2'] + combined_control_df['rpm_3'] + combined_control_df['rpm_4']) / 4 * 3.14 * 0.125 * 60.0 / 1000.0\n",
+    "combined_control_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ccd0427",
+   "metadata": {},
+   "source": [
+    "#### Total Duration for Each City"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "28c756dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['London', 'Bern', 'Liu Zhou Shi', 'California', 'Madrid',\n",
+       "       'Stockholm', 'Vienna', 'Berlin', 'Wuhan', 'Taipei', 'Singapore',\n",
+       "       'Belgium', 'San Diego', 'Bobigny', 'Paris'], dtype=object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# location data\n",
+    "location_df = pd.read_csv('./data/location.csv')\n",
+    "location_df['city'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "64fef66e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Create a new DataFrame that records the start and end time of each unique session\n",
+    "session_df = combined_control_df.groupby('Session').agg({'timestamp': ['min', 'max'], 'Robot_ID': 'first'})\n",
+    "\n",
+    "# Flatten the MultiIndex in columns\n",
+    "session_df.columns = ['_'.join(col).strip() for col in session_df.columns.values]\n",
+    "\n",
+    "# Reset the index\n",
+    "session_df.reset_index(inplace=True)\n",
+    "\n",
+    "# Convert the 'timestamp_min' and 'timestamp_max' columns to datetime format\n",
+    "session_df['timestamp_min'] = pd.to_datetime(session_df['timestamp_min'], unit='s')\n",
+    "session_df['timestamp_max'] = pd.to_datetime(session_df['timestamp_max'], unit='s')\n",
+    "\n",
+    "# Calculate the duration for each of these unique sessions\n",
+    "session_df['session_hours'] = (session_df['timestamp_max'] - session_df['timestamp_min']).dt.total_seconds() / 3600  # convert seconds to hours\n",
+    "\n",
+    "# Merge session data with location data\n",
+    "merged_df = pd.merge(session_df, location_df, left_on='Robot_ID_first', right_on='bot_id', how='left')\n",
+    "\n",
+    "# Group by city and sum the session_hours\n",
+    "total_duration_per_city = merged_df.groupby('city')['session_hours'].sum()\n",
+    "\n",
+    "# Sort the cities by total duration\n",
+    "total_duration_per_city_sorted = total_duration_per_city.sort_values(ascending=False)\n",
+    "\n",
+    "# Create a bar plot of total duration per city\n",
+    "total_duration_per_city_sorted.plot(kind='bar', figsize=(10, 6))\n",
+    "plt.xlabel('City')\n",
+    "plt.ylabel('Total Duration (hours)')\n",
+    "plt.title('Total Duration for each City')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3281202e",
+   "metadata": {},
+   "source": [
+    "#### Session Duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "377283ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of sessions: 1864\n",
+      "Total duration in hours: 344.5340849968355\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate session duration\n",
+    "combined_control_df['timestamp'] = pd.to_datetime(combined_control_df['timestamp'], unit='s')  # convert unix timestamp to datetime\n",
+    "grouped = combined_control_df.groupby('Session')\n",
+    "session_duration = grouped['timestamp'].max() - grouped['timestamp'].min()\n",
+    "\n",
+    "# convert duration to seconds for easier analysis and comparison\n",
+    "session_duration_in_seconds = session_duration.dt.total_seconds()\n",
+    "# convert duration to minutes for easier analysis and comparison\n",
+    "session_duration_in_minutes = session_duration.dt.total_seconds() / 60\n",
+    "\n",
+    "# convert Series to DataFrame and rename the column\n",
+    "session_duration_df = session_duration_in_minutes.to_frame().reset_index()\n",
+    "session_duration_df.columns = ['Session', 'Session Minutes']\n",
+    "\n",
+    "# Calculate the total number of sessions\n",
+    "total_sessions = len(session_duration_df)\n",
+    "\n",
+    "# Calculate the total number of hours\n",
+    "total_hours = session_duration_df['Session Minutes'].sum() / 60\n",
+    "\n",
+    "print(f'Total number of sessions: {total_sessions}')\n",
+    "print(f'Total duration in hours: {total_hours}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4c5b7962",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Estimate the maximum session duration in minutes\n",
+    "max_duration = session_duration_df['Session Minutes'].max()\n",
+    "\n",
+    "plt.figure(figsize=(15, 6))  # Adjust the figure size\n",
+    "\n",
+    "# Plot the histogram with one bin per 2 minutes\n",
+    "session_duration_df['Session Minutes'].hist(bins=int(max_duration / 2), edgecolor='black')\n",
+    "\n",
+    "# Add x-axis ticks\n",
+    "plt.xticks(np.arange(0, max_duration + 10, 10))  # +10 in max_duration to ensure the last tick is included\n",
+    "\n",
+    "plt.title(\"Histogram of Session Durations\")\n",
+    "plt.xlabel(\"Duration in Minutes\")\n",
+    "plt.ylabel(\"Number of Sessions\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d66fbf8",
+   "metadata": {},
+   "source": [
+    "#### Speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "46ade2c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the number of bins\n",
+    "num_bins = 100\n",
+    "\n",
+    "# Calculate histogram\n",
+    "counts, bin_edges = np.histogram(combined_control_df['actual_speed'], bins=num_bins, range=(0, 10))\n",
+    "\n",
+    "# Convert counts to hours\n",
+    "counts_in_hours = (counts * 0.1) / 3600\n",
+    "\n",
+    "# Calculate total hours\n",
+    "total_hours = counts_in_hours.sum()\n",
+    "\n",
+    "# Plot the histogram\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(bin_edges[:-1], counts_in_hours, width = 0.1, edgecolor='black')\n",
+    "plt.xlabel('Actual Speed (km/h)')\n",
+    "plt.ylabel('Hours')\n",
+    "plt.title(f'Histogram of Actual Speeds (Range 0-10km/h)\\nTotal Hours: {total_hours:.2f}')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Set x-axis range\n",
+    "plt.xlim(0, 10)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec1e8ba4",
+   "metadata": {},
+   "source": [
+    "Range 0.1-10 km/h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9a207c87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate histogram\n",
+    "counts, bin_edges = np.histogram(combined_control_df['actual_speed'], bins=num_bins, range=(0.1, 10))\n",
+    "\n",
+    "# Convert counts to hours\n",
+    "counts_in_hours = (counts * 0.1) / 3600\n",
+    "\n",
+    "# Calculate total hours\n",
+    "total_hours = counts_in_hours.sum()\n",
+    "\n",
+    "# Plot the histogram\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(bin_edges[:-1], counts_in_hours, width = 0.1, edgecolor='black')\n",
+    "plt.xlabel('Actual Speed (km/h)')\n",
+    "plt.ylabel('Hours')\n",
+    "plt.title(f'Histogram of Actual Speeds (Range 0.1-10km/h)\\nTotal Hours: {total_hours:.2f}')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Set x-axis range\n",
+    "plt.xlim(0, 10)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}