+20 ML Algorithms +15 Plot for Beginners

added new dataset

Author: mbahmani

+20 ML Algorithms +15 Plot for Beginners/+20 ML Algorithms +15 Plot for Beginners.ipynb

@@ -5,7 +5,7 @@
         "_uuid": "a8f9622945156d6337ba73c481da2de7efef7384"
       },
       "cell_type": "markdown",
-      "source": "## <div style=\"text-align: center\"> +20 ML Algorithms +15 Plot for Beginners</div>\n\n<div style=\"text-align: center\"><b>Quite Practical and Far from any Theoretical Concepts</b></div>\n<img src='https://image.ibb.co/gbH3ue/iris.png'>\n<div style=\"text-align:center\">last update: <b>17/12/2018</b></div>\n\n\n\n>###### you may  be interested have a look at it: [**Course Home Page**](https://www.kaggle.com/mjbahmani/10-steps-to-become-a-data-scientist)\n\n\n---------------------------------------------------------------------\nyou can Fork and Run this kernel on Github:\n> ###### [ GitHub](https://github.com/mjbahmani)\n\n-------------------------------------------------------------------------------------------------------------\n\n **I hope you find this kernel helpful and some <font color=\"red\"><b>UPVOTES</b></font> would be very much appreciated**\n \n -----------"
+      "source": "## <div style=\"text-align: center\"> +20 ML Algorithms +15 Plot for Beginners</div>\n\n<div style=\"text-align: center\"><b>Quite Practical and Far from any Theoretical Concepts</b></div>\n<img src='https://image.ibb.co/gbH3ue/iris.png'>\n<div style=\"text-align:center\">last update: <b>12/02/2019</b></div>\n\n\n\n> You are reading **10 Steps to Become a Data Scientist** and are now in the 9th step : \n\n1. [Leren Python](https://www.kaggle.com/mjbahmani/the-data-scientist-s-toolbox-tutorial-1)\n2. [Python Packages](https://www.kaggle.com/mjbahmani/the-data-scientist-s-toolbox-tutorial-2)\n3. [Mathematics and Linear Algebra](https://www.kaggle.com/mjbahmani/linear-algebra-for-data-scientists)\n4. [Programming &amp; Analysis Tools](https://www.kaggle.com/mjbahmani/20-ml-algorithms-15-plot-for-beginners)\n5. [Big Data](https://www.kaggle.com/mjbahmani/a-data-science-framework-for-quora)\n6. [Data visualization](https://www.kaggle.com/mjbahmani/top-5-data-visualization-libraries-tutorial)\n7. [Data Cleaning](https://www.kaggle.com/mjbahmani/machine-learning-workflow-for-house-prices)\n8. [How to solve a Problem?](https://www.kaggle.com/mjbahmani/the-data-scientist-s-toolbox-tutorial-2)\n9. <font color=\"red\">You are in the ninth step</font>\n10. [Deep Learning](https://www.kaggle.com/mjbahmani/top-5-deep-learning-frameworks-tutorial)\n\n\n---------------------------------------------------------------------\nyou can Fork and Run this kernel on Github:\n> ###### [ GitHub](https://github.com/mjbahmani)\n\n-------------------------------------------------------------------------------------------------------------\n\n **I hope you find this kernel helpful and some <font color=\"red\"><b>UPVOTES</b></font> would be very much appreciated**\n \n -----------"
     },
     {
       "metadata": {
@@ -131,14 +131,24 @@
       "cell_type": "markdown",
       "source": "<a id=\"61\"></a> <br>\n## 6-1 Data Collection\n**Iris dataset**  consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray\n\nThe rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and Petal Width.[6]\n"
     },
+    {
+      "metadata": {
+        "trusted": true,
+        "_uuid": "944133064c837642bf25fe32447502d55b1a0f07"
+      },
+      "cell_type": "code",
+      "source": "print(os.listdir(\"../input/\"))",
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "metadata": {
         "_uuid": "9269ae851b744856bce56840637030a16a5877e1",
         "trusted": true,
         "_kg_hide-input": true
       },
       "cell_type": "code",
-      "source": "# import Dataset to play with it\ndataset = pd.read_csv('../input/Iris.csv')",
+      "source": "# import Dataset to play with it\ndataset = pd.read_csv('../input/iris-dataset/Iris.csv')\niris_Species = pd.read_csv('../input/iris/Iris.csv')",
       "execution_count": null,
       "outputs": []
     },
@@ -167,6 +177,16 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "metadata": {
+        "trusted": true,
+        "_uuid": "899065374387b9676210933e83aaeaf2b1f3ab19"
+      },
+      "cell_type": "code",
+      "source": "type(iris_Species)",
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "metadata": {
         "_uuid": "055772bd170aa8018aabd85106b76675802c33b3"
@@ -1857,7 +1877,7 @@
         "_uuid": "8c48de6290f33a7e89956a28d05cfac79b0cb57e"
       },
       "cell_type": "markdown",
-      "source": ">If you have read the notebook, you can follow next steps: [Course Home Page](https://www.kaggle.com/mjbahmani/10-steps-to-become-a-data-scientist)"
+      "source": "Go to first step: [**Course Home Page**](https://www.kaggle.com/mjbahmani/10-steps-to-become-a-data-scientist)\n\nGo to next step : [**Mathematics and Linear Algebra**](https://www.kaggle.com/mjbahmani/linear-algebra-for-data-scientists)"
     }
   ],
   "metadata": {