diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 31ff6c5..f0119bc 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -33,17 +33,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "from scipy.stats import bernoulli\n",
+    "from scipy.stats import binom\n",
+    "from scipy.stats import poisson\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "# Viz mantra\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "%config Inlinebackend.figure_format = 'retina'\n",
+    "\n",
+    "import seaborn as sns\n",
+    "sns.set_context('poster')\n",
+    "sns.set(rc={'figure.figsize': (16., 9.)})\n",
+    "sns.set_style('whitegrid')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6\n",
+      "0.4\n"
+     ]
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "Calculate:\n",
     "p = probability that the fruit is an apple \n",
     "q = probability that the fruit is an orange\n",
     "\"\"\"\n",
+    "apples = 60\n",
+    "oranges = 40\n",
+    "total = 100\n",
     "\n",
-    "# your code here"
+    "print(apples/total)\n",
+    "print(oranges / total)\n",
+    "\n"
    ]
   },
   {
@@ -61,11 +98,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.07775999999999998\n",
+      "8.349416423424006e-08\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "p = 0.6\n",
+    "q = 0.4\n",
+    "n = 5\n",
+    "my_bernoulli = bernoulli (p)\n",
+    "my_bernoulli.rvs(n)\n",
+    "\n",
+    "# What is the probability that the first 5 fruits are all apples?\n",
+    "\n",
+    "print(p**5)\n",
+    "\n",
+    "# What is the probability that the first 5 fruits are all apples and the next 15 fruits are all oranges?\n",
+    "\n",
+    "print(p**5 * q**15)\n"
    ]
   },
   {
@@ -83,11 +141,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0012944935222876587"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "N = 20\n",
+    "p = 0.6\n",
+    "\n",
+    "my_binomial = binom(N, p)\n",
+    "my_binomial_experiment = my_binomial.rvs(1) # Creating an experiment based on those conditions\n",
+    "my_binomial.pmf(5)\n",
+    "\n"
    ]
   },
   {
@@ -101,11 +176,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00031703112116863004"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "\n",
+    "my_binomial.cdf(4)\n"
    ]
   },
   {
@@ -119,12 +206,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n",
-    "# Please label the axes and give a title to the plot "
+    "x = np.arange(0, 20)\n",
+    "plt.plot(x, my_binomial.pmf(x), \"bo\")\n",
+    "plt.vlines(x, 0, my_binomial.pmf(x), colors=\"b\", lw=5, alpha=0.5)\n",
+    "plt.title(\"Theoretical probability distribution of N=100, p=0.02\", size=20);"
    ]
   },
   {
@@ -144,11 +244,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here "
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.053775025581946814"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lam = 2.3 # 2.3 goals per match\n",
+    "champions = poisson(lam)\n",
+    "\n",
+    "champions.pmf(5)"
    ]
   },
   {
@@ -160,12 +283,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n",
-    "# Please label the axes and give a title to the plot "
+    "x = np.arange(0, 10) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+    "plt.plot(x, champions.pmf(x), \"bo\")\n",
+    "plt.vlines(x, 0, champions.pmf(x), colors=\"b\", lw=5, alpha=0.5)\n",
+    "plt.title(\"Theoretical probability distribution of Champions Goals\", size=20);\n",
+    "\n"
    ]
   }
  ],
@@ -185,7 +322,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,