eda and model training

notebook/EDA.ipynb

@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1123,7 +1123,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1134,7 +1134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1197,7 +1197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1206,7 +1206,7 @@
        "<Axes: ylabel='writing_score'>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1253,7 +1253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1344,12 +1344,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x1000 with 2 Axes>"
       ]
@@ -1364,15 +1364,22 @@
     "for container in ax[0].containers:\n",
     "    ax[0].bar_label(container,color='black',size=0.2)\n",
     "\n",
-    "plt.pie(x=df.gender.value_counts(),labels=df.gender.value_counts().index \\\n",
-    "        explode=  )"
+    "plt.pie(x=df.gender.value_counts(),labels=df.gender.value_counts().index, \\\n",
+    "        explode=[0,0.1],autopct='%1.1f%%' )\n",
+    "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Observation\n",
+    "- Gender has balanced data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],

notebook/catboost_info/catboost_training.json

@@ -0,0 +1,1004 @@
+{
+"meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":1000,"learn_sets":["learn"],"name":"experiment"},
+"iterations":[
+{"learn":[15.1265543],"iteration":0,"passed_time":0.0005210452513,"remaining_time":0.5205242061},
+{"learn":[14.76531882],"iteration":1,"passed_time":0.0008293977145,"remaining_time":0.4138694595},
+{"learn":[14.3879254],"iteration":2,"passed_time":0.001218244295,"remaining_time":0.4048631872},
+{"learn":[14.01588715],"iteration":3,"passed_time":0.001536679354,"remaining_time":0.3826331592},
+{"learn":[13.73314153],"iteration":4,"passed_time":0.001818242109,"remaining_time":0.3618301796},
+{"learn":[13.45632399],"iteration":5,"passed_time":0.002126052945,"remaining_time":0.3522161045},
+{"learn":[13.15514533],"iteration":6,"passed_time":0.002483818463,"remaining_time":0.3523473906},
+{"learn":[12.86146543],"iteration":7,"passed_time":0.002792795881,"remaining_time":0.3463066892},
+{"learn":[12.59672108],"iteration":8,"passed_time":0.00310868946,"remaining_time":0.3423012505},
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notebook/catboost_info/time_left.tsv

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requirement.txt

@@ -1,7 +1,54 @@
+appnope==0.1.3
+asttokens==2.4.0
+backcall==0.2.0
+catboost==1.2.1
+comm==0.1.4
+contourpy==1.1.0
+cycler==0.11.0
+debugpy==1.6.7.post1
+decorator==5.1.1
+exceptiongroup==1.1.3
+executing==1.2.0
+fonttools==4.42.1
+graphviz==0.20.1
+ipykernel==6.25.2
+ipython==8.15.0
+jedi==0.19.0
+joblib==1.3.2
+jupyter_client==8.3.1
+jupyter_core==5.3.1
+kiwisolver==1.4.5
+matplotlib==3.7.2
+matplotlib-inline==0.1.6
+-e git+https://github.com/SinghJagpreet096/mlops@b7a4d5146929c485a98e014a495fd15b866a6528#egg=mlops
+nest-asyncio==1.5.7
 numpy==1.25.2
+packaging==23.1
 pandas==2.0.3
+parso==0.8.3
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==10.0.0
+platformdirs==3.10.0
+plotly==5.16.1
+prompt-toolkit==3.0.39
+psutil==5.9.5
+ptyprocess==0.7.0
+pure-eval==0.2.2
+Pygments==2.16.1
+pyparsing==3.0.9
 python-dateutil==2.8.2
 pytz==2023.3
+pyzmq==25.1.1
+scikit-learn==1.3.0
+scipy==1.11.2
+seaborn==0.12.2
 six==1.16.0
+stack-data==0.6.2
+tenacity==8.2.3
+threadpoolctl==3.2.0
+tornado==6.3.3
+traitlets==5.9.0
 tzdata==2023.3
--e .
+wcwidth==0.2.6
+xgboost==1.7.6