diff --git a/Visualization/data_visualizing.ipynb b/Visualization/data_visualizing.ipynb
new file mode 100644
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+++ b/Visualization/data_visualizing.ipynb
@@ -0,0 +1,520 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "data visualizing.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QGwLHIT6SopH"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SW8pf6sqS77g"
+      },
+      "source": [
+        "data = pd.read_csv(\"data.csv\")"
+      ],
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "iTxb9ESmTJLf",
+        "outputId": "df21404d-4179-481f-8858-94b63667f9d1"
+      },
+      "source": [
+        "data.head(1)"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2020-10-09</td>\n",
+              "      <td>115.279999</td>\n",
+              "      <td>117.0</td>\n",
+              "      <td>114.919998</td>\n",
+              "      <td>116.970001</td>\n",
+              "      <td>116.222481</td>\n",
+              "      <td>100506900</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Date        Open   High         Low       Close   Adj Close     Volume\n",
+              "0  2020-10-09  115.279999  117.0  114.919998  116.970001  116.222481  100506900"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ic7IANH6cEWC"
+      },
+      "source": [
+        "##Summary"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TmUsHEL2TKoQ",
+        "outputId": "51473654-d47e-4fb9-99b9-b6e0bb320126"
+      },
+      "source": [
+        "data.info()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 252 entries, 0 to 251\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column     Non-Null Count  Dtype  \n",
+            "---  ------     --------------  -----  \n",
+            " 0   Date       252 non-null    object \n",
+            " 1   Open       252 non-null    float64\n",
+            " 2   High       252 non-null    float64\n",
+            " 3   Low        252 non-null    float64\n",
+            " 4   Close      252 non-null    float64\n",
+            " 5   Adj Close  252 non-null    float64\n",
+            " 6   Volume     252 non-null    int64  \n",
+            "dtypes: float64(5), int64(1), object(1)\n",
+            "memory usage: 13.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "id": "w2aGpCM2TNH1",
+        "outputId": "c49ee19f-0825-4cda-e78c-430ac31ca061"
+      },
+      "source": [
+        "data.describe()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>252.000000</td>\n",
+              "      <td>252.000000</td>\n",
+              "      <td>252.000000</td>\n",
+              "      <td>252.000000</td>\n",
+              "      <td>252.000000</td>\n",
+              "      <td>2.520000e+02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>132.047382</td>\n",
+              "      <td>133.423691</td>\n",
+              "      <td>130.605714</td>\n",
+              "      <td>132.041746</td>\n",
+              "      <td>131.686555</td>\n",
+              "      <td>9.610568e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>11.046097</td>\n",
+              "      <td>10.968939</td>\n",
+              "      <td>11.075379</td>\n",
+              "      <td>11.057846</td>\n",
+              "      <td>11.222588</td>\n",
+              "      <td>3.247330e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>109.110001</td>\n",
+              "      <td>110.680000</td>\n",
+              "      <td>107.320000</td>\n",
+              "      <td>108.769997</td>\n",
+              "      <td>108.074883</td>\n",
+              "      <td>4.639770e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>123.475000</td>\n",
+              "      <td>124.849998</td>\n",
+              "      <td>121.764997</td>\n",
+              "      <td>123.199999</td>\n",
+              "      <td>122.649681</td>\n",
+              "      <td>7.455655e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>130.334999</td>\n",
+              "      <td>131.685005</td>\n",
+              "      <td>129.005005</td>\n",
+              "      <td>130.864997</td>\n",
+              "      <td>130.296272</td>\n",
+              "      <td>8.991345e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>142.827499</td>\n",
+              "      <td>144.189995</td>\n",
+              "      <td>140.819999</td>\n",
+              "      <td>142.499996</td>\n",
+              "      <td>142.240574</td>\n",
+              "      <td>1.116616e+08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>156.979996</td>\n",
+              "      <td>157.259995</td>\n",
+              "      <td>154.389999</td>\n",
+              "      <td>156.690002</td>\n",
+              "      <td>156.690002</td>\n",
+              "      <td>2.623305e+08</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "             Open        High         Low       Close   Adj Close        Volume\n",
+              "count  252.000000  252.000000  252.000000  252.000000  252.000000  2.520000e+02\n",
+              "mean   132.047382  133.423691  130.605714  132.041746  131.686555  9.610568e+07\n",
+              "std     11.046097   10.968939   11.075379   11.057846   11.222588  3.247330e+07\n",
+              "min    109.110001  110.680000  107.320000  108.769997  108.074883  4.639770e+07\n",
+              "25%    123.475000  124.849998  121.764997  123.199999  122.649681  7.455655e+07\n",
+              "50%    130.334999  131.685005  129.005005  130.864997  130.296272  8.991345e+07\n",
+              "75%    142.827499  144.189995  140.819999  142.499996  142.240574  1.116616e+08\n",
+              "max    156.979996  157.259995  154.389999  156.690002  156.690002  2.623305e+08"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "C-wteainT9-X"
+      },
+      "source": [
+        "df = data.copy()\n",
+        "df = df.iloc[:,1:]"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "soG6IwQZb7T2"
+      },
+      "source": [
+        "##Histogram"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "okXiPDHNU69z",
+        "outputId": "2d70cd86-b0da-4077-d7bb-cd38e40aac24"
+      },
+      "source": [
+        "columns = df.columns.values\n",
+        "len(columns)//3 +1"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "3"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 775
+        },
+        "id": "ntETccAaTQc8",
+        "outputId": "e68ae424-b559-4e6b-f7a1-7a9e0ce77589"
+      },
+      "source": [
+        "columns = df.columns.values\n",
+        "# Calculating required amount of rows to display all feature plots\n",
+        "cols = 3\n",
+        "rows = len(columns) // cols + 1\n",
+        "\n",
+        "fig, axs = plt.subplots(ncols=cols, nrows=rows, figsize=(16,20), sharex=False)\n",
+        "\n",
+        "# Adding some distance between plots\n",
+        "plt.subplots_adjust(hspace = 0.3)\n",
+        "\n",
+        "# Plots counter\n",
+        "i=0\n",
+        "for r in np.arange(0, rows, 1):\n",
+        "    for c in np.arange(0, cols, 1):\n",
+        "        if i >= len(columns): # If there is no more data columns to make plots from\n",
+        "            axs[r, c].set_visible(False) # Hiding axes so there will be clean background\n",
+        "        else:\n",
+        "            # Train data histogram\n",
+        "            hist1 = axs[r, c].hist(df[columns[i]].values,\n",
+        "                                   range=(df[columns[i]].min(),\n",
+        "                                          df[columns[i]].max()),\n",
+        "                                   bins=40,\n",
+        "                                   color=\"deepskyblue\",\n",
+        "                                   edgecolor=\"black\",\n",
+        "                                   alpha=0.7,\n",
+        "                                   label=\"Train Dataset\")\n",
+        "            axs[r, c].set_title(columns[i], fontsize=14, pad=5)\n",
+        "            axs[r, c].tick_params(axis=\"y\", labelsize=13)\n",
+        "            axs[r, c].tick_params(axis=\"x\", labelsize=13)\n",
+        "            axs[r, c].grid(axis=\"y\")\n",
+        "            i+=1\n",
+        "            "
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1152x1440 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Xx97-0n2b28p"
+      },
+      "source": [
+        "##Box Plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "id": "HF2j0VR-ZWl2",
+        "outputId": "e6de2c0a-0703-4197-c832-1b7ae5b85dd8"
+      },
+      "source": [
+        "data[['Open','High','Low','Close','Adj Close']].plot(kind='box')"
+      ],
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa289b97150>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yaHHMlb4b0wp"
+      },
+      "source": [
+        "##Heat Map"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 865
+        },
+        "id": "efuggkJZUCDl",
+        "outputId": "b2406976-b5b7-48e1-ce9f-e0bf1793e3e2"
+      },
+      "source": [
+        "# Calculatin correlation values\n",
+        "df = df.corr().round(2)\n",
+        "\n",
+        "# Mask to hide upper-right part of plot as it is a duplicate\n",
+        "mask = np.zeros_like(df)\n",
+        "mask[np.triu_indices_from(mask)] = True\n",
+        "\n",
+        "# Making a plot\n",
+        "plt.figure(figsize=(14,14))\n",
+        "ax = sns.heatmap(df, annot=True, mask=mask, cmap=\"RdBu\", annot_kws={\"weight\": \"normal\", \"fontsize\":9})\n",
+        "ax.set_title(\"Feature correlation heatmap\", fontsize=17)\n",
+        "plt.setp(ax.get_xticklabels(), rotation=90, ha=\"right\",\n",
+        "         rotation_mode=\"anchor\", weight=\"normal\")\n",
+        "plt.setp(ax.get_yticklabels(), weight=\"normal\",\n",
+        "         rotation_mode=\"anchor\", rotation=0, ha=\"right\")\n",
+        "plt.show();"
+      ],
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1008x1008 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "av6QCg0VXS5u"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}