Activity-Bot-Score.ipynb

{
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       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>count</th>\n",
       "      <th>activity</th>\n",
       "      <th>bot_score_english</th>\n",
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      "text/plain": [
       "          user_id   age  count   activity  bot_score_english\n",
       "0           13616  4256  23279   5.469690           0.020441\n",
       "1          614513  4124    525   0.127304           0.073133\n",
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      ]
     },
     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Read file\n",
    "bot_data = pd.read_csv('activity_botscore.csv')\n",
    "bot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d95b2a5-800c-441a-b747-50f8118d7c55",
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    {
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      "text/plain": [
       "   user_id   age  count  activity  bot_score_english\n",
       "0    13616  4256  23279  5.469690           0.020441\n",
       "1   614513  4124    525  0.127304           0.073133\n",
       "2     5565  4313   8146  1.888709           0.041325\n",
       "3    19783  4226   3510  0.830573           0.014311\n",
       "4     2631  4351   8146  1.872213           0.058340"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "bot_data.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "e105f43a-9581-4729-9739-3ba3de021fbd",
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      "text/plain": [
       "          user_id   age  count  bot_score_english\n",
       "0           13616  4256  23279           0.020441\n",
       "1          614513  4124    525           0.073133\n",
       "2            5565  4313   8146           0.041325\n",
       "3           19783  4226   3510           0.014311\n",
       "4            2631  4351   8146           0.058340\n",
       "...           ...   ...    ...                ...\n",
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       "11186  4918929727  3077  26841           0.010001\n",
       "11187  4920512909  3077  44037           0.025888\n",
       "11188  4910272047  3077   5780           0.058340\n",
       "11189  4923560931  3077  24936           0.091313\n",
       "\n",
       "[11190 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Input & output data\n",
    "X = bot_data.drop(columns=['activity'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe0bd514-2675-4cc1-9d41-e25b5cb25aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         5.469690\n",
       "1         0.127304\n",
       "2         1.888709\n",
       "3         0.830573\n",
       "4         1.872213\n",
       "           ...    \n",
       "11185     6.594085\n",
       "11186     8.723107\n",
       "11187    14.311667\n",
       "11188     1.878453\n",
       "11189     8.103997\n",
       "Name: activity, Length: 11190, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = bot_data['activity']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1229837a-c7eb-4dea-9646-45b6fae4bc69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2238, 4), (2238,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#Train test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)\n",
    "X_train.shape, y_train.shape\n",
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74a50246-25d7-44ad-90d1-d1fe3855ae62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.06525773, 11.78853659,  4.29107323, ...,  4.07580763,\n",
       "        1.41509593,  2.0781392 ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "#Build, fit & evaluate model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b9941f92-eb82-4e03-9e00-3d145431b573",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error:  11.407692247471992\n",
      "Mean Absolute Error:  1.3644377044605611\n",
      "Intercept:  16.16106023638988\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "mae = mean_absolute_error(y_test, predictions)\n",
    "intercept = model.intercept_\n",
    "print(\"Mean Squared Error: \", mse)\n",
    "print(\"Mean Absolute Error: \", mae)\n",
    "print(\"Intercept: \", intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca958a7e-02e6-4179-bd95-0f9a63f7d487",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#Visualize results\n",
    "plt.scatter(y_test, predictions, alpha=0.5)\n",
    "plt.plot([y.min(), y.max()], [y.min(), y.max()], color='red')\n",
    "plt.xlabel('Actual values')\n",
    "plt.ylabel('Predicted values')\n",
    "plt.title('Linear Regression Botscore')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ceb2d97-cea4-4536-9961-72fa4e4c9408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualise Residual(Loss)\n",
    "plt.scatter(predictions, y_test - predictions, alpha=0.5)\n",
    "plt.xlabel('Actual values')\n",
    "plt.ylabel('Predicted values')\n",
    "plt.title('Residual')\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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}