FS6 - Univariance Selection.ipynb

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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "2            563     1          0.5         1   2       1          41    0.9   \n",
       "3            615     1          2.5         0   0       0          10    0.8   \n",
       "4           1821     1          1.2         0  13       1          44    0.6   \n",
       "\n",
       "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2  ...         20       756  2549     9     7         19   \n",
       "1        136        3  ...        905      1988  2631    17     3          7   \n",
       "2        145        5  ...       1263      1716  2603    11     2          9   \n",
       "3        131        6  ...       1216      1786  2769    16     8         11   \n",
       "4        141        2  ...       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  price_range  \n",
       "0        0             0     1            1  \n",
       "1        1             1     0            2  \n",
       "2        1             1     0            2  \n",
       "3        1             0     0            2  \n",
       "4        1             1     0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('Datasets\\mobile_dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d561549b",
   "metadata": {},
   "source": [
    "### Check Duplicate Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f829afc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_dup_col(df):\n",
    "    duplicate_columns = set()\n",
    "    for x in range(df.shape[1]):\n",
    "        col = df.iloc[:, x]\n",
    "        for y in range(x + 1, df.shape[1]):\n",
    "            other_col = df.iloc[:, y]\n",
    "            if col.equals(other_col):\n",
    "                duplicate_columns.add(df.columns.values[y])\n",
    "    \n",
    "    # Print the duplicate columns\n",
    "    if duplicate_columns:\n",
    "        print(\"Duplicate columns found:\")\n",
    "        print(duplicate_columns)\n",
    "    else:\n",
    "        print(\"No duplicate columns found.\")          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "64b016b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No duplicate columns found.\n"
     ]
    }
   ],
   "source": [
    "check_dup_col(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86433b37",
   "metadata": {},
   "source": [
    "### Checking Duplicate Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9ad333ed",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
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       "<p>0 rows × 21 columns</p>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [battery_power, blue, clock_speed, dual_sim, fc, four_g, int_memory, m_dep, mobile_wt, n_cores, pc, px_height, px_width, ram, sc_h, sc_w, talk_time, three_g, touch_screen, wifi, price_range]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "df[df.duplicated()]\n",
    "#No duplicate rows in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f28d1b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "84d62e79",
   "metadata": {},
   "source": [
    "### Univariate Selection\n",
    "\n",
    "\n",
    "Univariate Selection, also known as Univariate Feature Selection, is a technique used in machine learning and feature selection to identify the most significant features or variables that contribute the most to the target variable. It involves evaluating each feature independently based on certain statistical measures and selecting the features with the highest scores.\n",
    "\n",
    "The basic idea behind Univariate Selection is to assess the relationship between each feature and the target variable individually, without considering the interactions or dependencies among features. It assumes that each feature is independent of the others and evaluates its predictive power solely based on its relationship with the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "286a78d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1]\n",
    "y = df[['price_range']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4073c590",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>pc</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "\n",
       "   mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2   2         20       756  2549     9     7         19   \n",
       "1        136        3   6        905      1988  2631    17     3          7   \n",
       "\n",
       "   three_g  touch_screen  wifi  \n",
       "0        0             0     1  \n",
       "1        1             1     0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a3e30ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price_range\n",
       "0            1\n",
       "1            2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d2c0c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c34c71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 21)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2fa48085",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Apply SelectKBest Algorithm\n",
    "ordered_rank_features= SelectKBest (score_func = chi2, k = 20)\n",
    "ordered_feature = ordered_rank_features.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21af3d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.41298666e+04, 7.23232323e-01, 6.48365906e-01, 6.31010795e-01,\n",
       "       1.01351665e+01, 1.52157239e+00, 8.98391244e+01, 7.45819631e-01,\n",
       "       9.59728626e+01, 9.09755558e+00, 9.18605355e+00, 1.73635695e+04,\n",
       "       9.81058675e+03, 9.31267519e+05, 9.61487832e+00, 1.64803191e+01,\n",
       "       1.32364000e+01, 3.27642810e-01, 1.92842942e+00, 4.22090730e-01])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_feature.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e0d85ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfscores = pd.DataFrame(ordered_feature.scores_, columns = [\"Score\"])\n",
    "dfcolumns = pd.DataFrame(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f8004eb5",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>battery_power</td>\n",
       "      <td>14129.866576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blue</td>\n",
       "      <td>0.723232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>clock_speed</td>\n",
       "      <td>0.648366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dual_sim</td>\n",
       "      <td>0.631011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fc</td>\n",
       "      <td>10.135166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>four_g</td>\n",
       "      <td>1.521572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>int_memory</td>\n",
       "      <td>89.839124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>m_dep</td>\n",
       "      <td>0.745820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mobile_wt</td>\n",
       "      <td>95.972863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>n_cores</td>\n",
       "      <td>9.097556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pc</td>\n",
       "      <td>9.186054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>px_height</td>\n",
       "      <td>17363.569536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>px_width</td>\n",
       "      <td>9810.586750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ram</td>\n",
       "      <td>931267.519053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sc_h</td>\n",
       "      <td>9.614878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sc_w</td>\n",
       "      <td>16.480319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>talk_time</td>\n",
       "      <td>13.236400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>three_g</td>\n",
       "      <td>0.327643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>touch_screen</td>\n",
       "      <td>1.928429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>wifi</td>\n",
       "      <td>0.422091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Features          Score\n",
       "0   battery_power   14129.866576\n",
       "1            blue       0.723232\n",
       "2     clock_speed       0.648366\n",
       "3        dual_sim       0.631011\n",
       "4              fc      10.135166\n",
       "5          four_g       1.521572\n",
       "6      int_memory      89.839124\n",
       "7           m_dep       0.745820\n",
       "8       mobile_wt      95.972863\n",
       "9         n_cores       9.097556\n",
       "10             pc       9.186054\n",
       "11      px_height   17363.569536\n",
       "12       px_width    9810.586750\n",
       "13            ram  931267.519053\n",
       "14           sc_h       9.614878\n",
       "15           sc_w      16.480319\n",
       "16      talk_time      13.236400\n",
       "17        three_g       0.327643\n",
       "18   touch_screen       1.928429\n",
       "19           wifi       0.422091"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank = pd.concat([dfcolumns, dfscores], axis= 1)\n",
    "features_rank.columns = ['Features', 'Score']\n",
    "features_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7a78da73",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ram</td>\n",
       "      <td>931267.519053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>px_height</td>\n",
       "      <td>17363.569536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>battery_power</td>\n",
       "      <td>14129.866576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>px_width</td>\n",
       "      <td>9810.586750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mobile_wt</td>\n",
       "      <td>95.972863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>int_memory</td>\n",
       "      <td>89.839124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sc_w</td>\n",
       "      <td>16.480319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>talk_time</td>\n",
       "      <td>13.236400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fc</td>\n",
       "      <td>10.135166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sc_h</td>\n",
       "      <td>9.614878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Features          Score\n",
       "13            ram  931267.519053\n",
       "11      px_height   17363.569536\n",
       "0   battery_power   14129.866576\n",
       "12       px_width    9810.586750\n",
       "8       mobile_wt      95.972863\n",
       "6      int_memory      89.839124\n",
       "15           sc_w      16.480319\n",
       "16      talk_time      13.236400\n",
       "4              fc      10.135166\n",
       "14           sc_h       9.614878"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank.nlargest(10, 'Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d6cc832",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "So, these are the top 10 important features of the dataset in terms of the dependancy of the target feature on train features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfd490ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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