space-voyager.ipynb

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    "# Project Name : Spaceship Titanic\n",
    "\n",
    "The **Spaceship Titanic** was an interstellar passenger liner launched a month ago. With almost 13,000 passengers on board, the vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.\n",
    "\n",
    "<p>\n",
    "    While rounding Alpha Centauri en route to its first destination—the torrid 55 Cancri E—the unwary Spaceship Titanic collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!\n",
    "    </p>\n",
    "\n",
    "## Problem Statement\n",
    "Predict which passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system."
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    "## Steps\n",
    "\n",
    "1. Firstly,  we will be importing the essential python libraries. "
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   "source": [
    "# Import helpful libraries\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
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    "2. Next, we will read the contents of training data provided and load it into python. And seperate the target. <br/>\n",
    "   Here, the outcome we require is the list of passengers who were transported by Anomaly. <br/>\n",
    "\n",
    "  "
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     "text": [
      "     PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n",
      "0        0001_01     Europa     False     B/0/P    TRAPPIST-1e  39.0  False   \n",
      "1        0002_01      Earth     False     F/0/S    TRAPPIST-1e  24.0  False   \n",
      "2        0003_01     Europa     False     A/0/S    TRAPPIST-1e  58.0   True   \n",
      "3        0003_02     Europa     False     A/0/S    TRAPPIST-1e  33.0  False   \n",
      "4        0004_01      Earth     False     F/1/S    TRAPPIST-1e  16.0  False   \n",
      "...          ...        ...       ...       ...            ...   ...    ...   \n",
      "8688     9276_01     Europa     False    A/98/P    55 Cancri e  41.0   True   \n",
      "8689     9278_01      Earth      True  G/1499/S  PSO J318.5-22  18.0  False   \n",
      "8690     9279_01      Earth     False  G/1500/S    TRAPPIST-1e  26.0  False   \n",
      "8691     9280_01     Europa     False   E/608/S    55 Cancri e  32.0  False   \n",
      "8692     9280_02     Europa     False   E/608/S    TRAPPIST-1e  44.0  False   \n",
      "\n",
      "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
      "0             0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
      "1           109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
      "2            43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
      "3             0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
      "4           303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
      "...           ...        ...           ...     ...     ...                ...   \n",
      "8688          0.0     6819.0           0.0  1643.0    74.0  Gravior Noxnuther   \n",
      "8689          0.0        0.0           0.0     0.0     0.0    Kurta Mondalley   \n",
      "8690          0.0        0.0        1872.0     1.0     0.0       Fayey Connon   \n",
      "8691          0.0     1049.0           0.0   353.0  3235.0   Celeon Hontichre   \n",
      "8692        126.0     4688.0           0.0     0.0    12.0   Propsh Hontichre   \n",
      "\n",
      "      Transported  \n",
      "0           False  \n",
      "1            True  \n",
      "2           False  \n",
      "3           False  \n",
      "4            True  \n",
      "...           ...  \n",
      "8688        False  \n",
      "8689        False  \n",
      "8690         True  \n",
      "8691        False  \n",
      "8692         True  \n",
      "\n",
      "[8693 rows x 14 columns]\n"
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    "# Load the data, and separate the target\n",
    "space_file_path = '../input/spaceship-titanic/train.csv'\n",
    "space_data = pd.read_csv(space_file_path)\n",
    "print(space_data)\n",
    "y = space_data.Transported\n"
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   "source": [
    "Before going further, let's look at the expected output of the prediction\n",
    "<br/>Hence, the final result should be a table containing **PassengerId** and **Transported** columns"
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Transported</th>\n",
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       "  PassengerId  Transported\n",
       "0     0013_01        False\n",
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   "source": [
    "sample_data = pd.read_csv(\"../input/spaceship-titanic/sample_submission.csv\")\n",
    "sample_data.head()"
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   "source": [
    "3. Creating X with the features that needs to taken consideration for the machine learning model."
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   "source": [
    "# Selecting features for X\n",
    "features = ['Age','Spa','ShoppingMall','FoodCourt','RoomService','VRDeck']\n"
   ]
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      "text/plain": [
       "<Figure size 2160x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " sns.relplot(\n",
    "    x=\"value\", y=\"Transported\", col=\"variable\", data=space_data.melt(id_vars=\"Transported\", value_vars=features), facet_kws=dict(sharex=False),\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b25329",
   "metadata": {
    "papermill": {
     "duration": 0.006127,
     "end_time": "2022-09-12T08:28:51.193558",
     "exception": false,
     "start_time": "2022-09-12T08:28:51.187431",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "4. Once the features are identified, we will be creating X based on the features. And fill any empty data cells in the input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40d75fb9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-12T08:28:51.208373Z",
     "iopub.status.busy": "2022-09-12T08:28:51.207459Z",
     "iopub.status.idle": "2022-09-12T08:28:51.213549Z",
     "shell.execute_reply": "2022-09-12T08:28:51.212571Z"
    },
    "papermill": {
     "duration": 0.015686,
     "end_time": "2022-09-12T08:28:51.215630",
     "exception": false,
     "start_time": "2022-09-12T08:28:51.199944",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Creating X based on features\n",
    "X = space_data[features]\n",
    "X=X.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "978544d0",
   "metadata": {
    "papermill": {
     "duration": 0.005981,
     "end_time": "2022-09-12T08:28:51.227922",
     "exception": false,
     "start_time": "2022-09-12T08:28:51.221941",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "5. Next, we will load the testing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe1c9564",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-12T08:28:51.243256Z",
     "iopub.status.busy": "2022-09-12T08:28:51.242625Z",
     "iopub.status.idle": "2022-09-12T08:28:51.288895Z",
     "shell.execute_reply": "2022-09-12T08:28:51.287672Z"
    },
    "papermill": {
     "duration": 0.057301,
     "end_time": "2022-09-12T08:28:51.291435",
     "exception": false,
     "start_time": "2022-09-12T08:28:51.234134",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Spa</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>VRDeck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.0</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6652.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Spa  ShoppingMall  FoodCourt  RoomService  VRDeck\n",
       "0  27.0     0.0           0.0        0.0          0.0     0.0\n",
       "1  19.0  2823.0           0.0        9.0          0.0     0.0\n",
       "2  31.0     0.0           0.0        0.0          0.0     0.0\n",
       "3  38.0   181.0           0.0     6652.0          0.0   585.0\n",
       "4  20.0     0.0         635.0        0.0         10.0     0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the test data\n",
    "test_data = pd.read_csv(\"../input/spaceship-titanic/test.csv\")\n",
    "test_data.head()\n",
    "test_X = test_data[features]\n",
    "test_X=test_X.fillna(0)\n",
    "test_X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "881f417d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-12T08:28:51.306712Z",
     "iopub.status.busy": "2022-09-12T08:28:51.306279Z",
     "iopub.status.idle": "2022-09-12T08:28:51.328258Z",
     "shell.execute_reply": "2022-09-12T08:28:51.327163Z"
    },
    "papermill": {
     "duration": 0.032997,
     "end_time": "2022-09-12T08:28:51.331126",
     "exception": false,
     "start_time": "2022-09-12T08:28:51.298129",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0013_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>G/3/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nelly Carsoning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0018_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/4/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lerome Peckers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0019_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>True</td>\n",
       "      <td>C/0/S</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>31.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sabih Unhearfus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0021_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>C/1/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>38.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6652.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>Meratz Caltilter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0023_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/5/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brence Harperez</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0013_01      Earth      True  G/3/S  TRAPPIST-1e  27.0  False   \n",
       "1     0018_01      Earth     False  F/4/S  TRAPPIST-1e  19.0  False   \n",
       "2     0019_01     Europa      True  C/0/S  55 Cancri e  31.0  False   \n",
       "3     0021_01     Europa     False  C/1/S  TRAPPIST-1e  38.0  False   \n",
       "4     0023_01      Earth     False  F/5/S  TRAPPIST-1e  20.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \n",
       "0          0.0        0.0           0.0     0.0     0.0   Nelly Carsoning  \n",
       "1          0.0        9.0           0.0  2823.0     0.0    Lerome Peckers  \n",
       "2          0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus  \n",
       "3          0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter  \n",
       "4         10.0        0.0         635.0     0.0     0.0   Brence Harperez  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8ac403",
   "metadata": {
    "papermill": {
     "duration": 0.006467,
     "end_time": "2022-09-12T08:28:51.344496",
     "exception": false,
     "start_time": "2022-09-12T08:28:51.338029",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "6. Though the best practises is to use the best and efficient machine learning model by using any validation techniques, here we will be using **Random Forest Regressor** model for acheiving the result.\n",
    "    \n",
    " # Random Forest Regressor Model\n",
    " > Random Forest Regression is a supervised learning algorithm that uses ensemble learning method for regression. \n",
    " > Ensemble learning method is a technique that combines predictions from multiple machine learning algorithms to make a more accurate prediction than a single model.\n",
    " \n",
    "The random forest uses many trees, and it makes a prediction by averaging the predictions of each component tree. \n",
    "It generally has much better predictive accuracy than a single decision tree and it works well with default parameters.\n",
    "\n",
    "<br> The steps for defining a random forest model are:\n",
    " - initialize the model into an object\n",
    " - fit the model with training data\n",
    " - predict the model with `model.predict()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "010143e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-12T08:28:51.360190Z",
     "iopub.status.busy": "2022-09-12T08:28:51.359287Z",
     "iopub.status.idle": "2022-09-12T08:28:52.894444Z",
     "shell.execute_reply": "2022-09-12T08:28:52.893397Z"
    },
    "papermill": {
     "duration": 1.545607,
     "end_time": "2022-09-12T08:28:52.896770",
     "exception": false,
     "start_time": "2022-09-12T08:28:51.351163",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a random forest model\n",
    "rf_model = RandomForestRegressor(random_state=1)\n",
    "rf_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "466bd195",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-12T08:28:52.912333Z",
     "iopub.status.busy": "2022-09-12T08:28:52.911606Z",
     "iopub.status.idle": "2022-09-12T08:28:52.989441Z",
     "shell.execute_reply": "2022-09-12T08:28:52.988434Z"
    },
    "papermill": {
     "duration": 0.088234,
     "end_time": "2022-09-12T08:28:52.991857",
     "exception": false,
     "start_time": "2022-09-12T08:28:52.903623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.86346201, 0.02      , 0.74364806, ..., 0.79279647, 0.62      ,\n",
       "       0.81365664])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting the output\n",
    "rf_val_predictions = rf_model.predict(test_X)\n",
    "Transportedvalues=[]\n",
    "rf_val_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb234e4",
   "metadata": {
    "papermill": {
     "duration": 0.007417,
     "end_time": "2022-09-12T08:28:53.006528",
     "exception": false,
     "start_time": "2022-09-12T08:28:52.999111",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Since the model produced numerical values, we will be creating predictions based on the numerical data which we have created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e42f76a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-12T08:28:53.022465Z",
     "iopub.status.busy": "2022-09-12T08:28:53.022061Z",
     "iopub.status.idle": "2022-09-12T08:28:53.028768Z",
     "shell.execute_reply": "2022-09-12T08:28:53.027756Z"
    },
    "papermill": {
     "duration": 0.017316,
     "end_time": "2022-09-12T08:28:53.030918",
     "exception": false,
     "start_time": "2022-09-12T08:28:53.013602",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for value in rf_val_predictions:\n",
    "    if value>.5:\n",
    "        Transportedvalues.append(True)\n",
    "    else :\n",
    "        Transportedvalues.append(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "52671561",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-12T08:28:53.045890Z",
     "iopub.status.busy": "2022-09-12T08:28:53.045548Z",
     "iopub.status.idle": "2022-09-12T08:28:53.052007Z",
     "shell.execute_reply": "2022-09-12T08:28:53.051223Z"
    },
    "papermill": {
     "duration": 0.017946,
     "end_time": "2022-09-12T08:28:53.055704",
     "exception": false,
     "start_time": "2022-09-12T08:28:53.037758",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86346201 0.02       0.74364806 ... 0.79279647 0.62       0.81365664]\n",
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     ]
    }
   ],
   "source": [
    "print(rf_val_predictions)\n",
    "print(Transportedvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ef2985",
   "metadata": {
    "papermill": {
     "duration": 0.006991,
     "end_time": "2022-09-12T08:28:53.071584",
     "exception": false,
     "start_time": "2022-09-12T08:28:53.064593",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Once the result is obtained from model, we will be creating a dataFrame to store the result in the indended format.\n",
    "<br/> After that we will be converting dataframe to csv as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "68bbaa93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-12T08:28:53.087589Z",
     "iopub.status.busy": "2022-09-12T08:28:53.087201Z",
     "iopub.status.idle": "2022-09-12T08:28:53.101531Z",
     "shell.execute_reply": "2022-09-12T08:28:53.100499Z"
    },
    "papermill": {
     "duration": 0.025222,
     "end_time": "2022-09-12T08:28:53.104102",
     "exception": false,
     "start_time": "2022-09-12T08:28:53.078880",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your submission was successfully completed!\n"
     ]
    }
   ],
   "source": [
    "#Printing the output\n",
    "output = pd.DataFrame({'PassengerId': test_data.PassengerId , 'Transported': Transportedvalues})\n",
    "output.to_csv('submission.csv', index=False)\n",
    "print(\"Your submission was successfully completed!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc07834",
   "metadata": {
    "papermill": {
     "duration": 0.007195,
     "end_time": "2022-09-12T08:28:53.118846",
     "exception": false,
     "start_time": "2022-09-12T08:28:53.111651",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Hence a machine learning model was created for predicting which passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.\n",
    "<br /> The accuracy of the model can be furthur increased by the following:\n",
    "- Adding or removing the features used \n",
    "- Using another machine learning model instead of Random Forest Regressor"
   ]
  }
 ],
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   "display_name": "Python 3",
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   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-09-12T08:28:39.144791",
   "version": "2.3.4"
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